Demand response through automated air conditioning in commercial buildings – a data-driven approach

Drasch, Benedict J.; Fridgen, Gilbert; Häfner, Lukas Article Demand response through automated air conditioning in commercial buildings – a data-driven approach Business Research Provided in Cooperation with: VHB - Verband der Hochschullehrer für Betriebswirtschaft, German Academic Association of Business Research Suggested Citation: Drasch, Benedict J.; Fridgen, Gilbert; Häfner, Lukas (2020) : Demand response through automated air conditioning in commercial buildings – a data-driven approach, Business Research, ISSN 2198-2627, Springer, Heidelberg, Vol. 13, Iss. 3, pp. 1491-1525, https://doi.org/10.1007/s40685-020-00122-0 This Version is available at: https://hdl.handle.net/10419/233188 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ ORIGINAL RESEARCH Demand response through automated air conditioning in commercial buildings—a data-driven approach Benedict J. Drasch 1 •Gilbert Fridgen 2 • Lukas Ha ¨fner 3 Received: 5 April 2020 / Accepted: 11 August 2020 / Published online: 4 September 2020 The Author(s) 2020 Abstract Building operation faces great challenges in electricity cost control as prices on electricity markets become increasingly volatile. Simultaneously, building operators could nowadays be empowered with information and communication technology that dynamically integrates relevant information sources, predicts future electricity prices and demand, and uses smart control to enable electricity cost savings. In particular, data-driven decision support systems would allow the utilization of temporal flexibilities in electricity consumption by shifting load to times of lower electricity prices. To contribute to this development, we propose a simple, general, and forward-looking demand response (DR) approach that can be part of future data-driven decision support systems in the domain of building electricity management. For the special use case of building air conditioning systems, our DR approach decides in periodic increments whether to exercise air conditioning in During a large part of the research activities associated with this paper, Gilbert Fridgen was Professor at the University of Bayreuth and Deputy Director of both, the FIM Research Center and the Project Group Business and Information Systems Engineering of the Fraunhofer FIT. &Benedict J. Drasch [email protected] Gilbert Fridgen [email protected] Lukas Ha ¨fner [email protected] 1 Project Group Business and Information Systems Engineering of the Fraunhofer FIT, FIM Research Center, University of Bayreuth, Wittelsbacherring 10, 95444 Bayreuth, Germany 2 SnT - Interdisciplinary Centre for Security, Reliability and Trust, Project Group Business and Information Systems Engineering of the Fraunhofer FIT, University of Luxembourg, 29 Avenue John F. Kennedy, 1855 Luxembourg, Germany 3 FIM Research Center, Project Group Business and Information Systems Engineering of the Fraunhofer FIT, University of Augsburg, Universitaetsstr. 12, 86159 Augsburg, Germany 123 Business Research (2020) 13:1491–1525 https://doi.org/10.1007/s40685-020-00122-0 regard to future electricity prices and demand. The decision is made based on an exante estimation by comparing the total expected electricity costs for all possible activation periods. For the prediction of future electricity prices, we draw on existing work and refine a prediction method for our purpose. To determine future electricity demand, we analyze historical data and derive data-driven dependencies. We embed the DR approach into a four-step framework and demonstrate its validity, utility and quality within an evaluation using real-world data from two public buildings in the US. Thereby, we address a real-world business case and find significant cost savings potential when using our DR approach. Keywords Information and communication technology Data-driven decision support Design science research Demand response in electricity markets Abbreviations Dtemperature Outside temperature—temp req am/pm Ante meridiem / post meridiem a/c Air conditioning aParameter for short-term adjustment AMI Advanced metering infrastructure C Electricity Costs C Degree Celsius cf Confer D Demand DPE Demand prediction error DR Demand response DSM Demand side management DSS Decision support system E Expectation value e.g Exempli gratia EI Energy informatics EPEX European Power Exchange et al Et alia h Hour ICT Information and communication technology ID Initial Demand i.e Id est K Kelvin kWh Kilo Watt hours LS Load shifting n Reference interval to compute a p Page PD Periodic demand S Spot price for electricity S Long-term mean electricity price $/€U.S. Dollar / Euro 1492 Business Research (2020) 13:1491–1525 123 T Occupancy time t Time of day t 0 First possible starting time for a/c t i A/c activation time t L Latest possible starting for a/c t m Current point in time temp req Required inside temperature hMean reversion speed x Restoration time for tempreq % Percent 1 Introduction To date, the energy transition is mostly pushed forward in advanced European economies (e.g., Germany, Norway, Sweden, Switzerland), but there is also a world-wide political endeavor (e.g., South America, Japan) to stop global warming (World Economic Forum 2017). With an increasing number of countries aiming for an entirely sustainable energy production (especially from wind and solar), sustainable energy sources evolved to be the world’s (relatively) fastest-growing energy source (U.S. Energy Information Administration 2018). The adverse effect of sustainable energy sources is their lack of controllability (e.g., sun shining, wind blowing), which brings volatility to energy supply (Goebel 2013; Ludig, et al. 2011). As a result, the expansion of sustainable energy sources results in more volatile electricity prices (Smith et al. 2010; Ketterer 2014). Additionally, the world’s energy consumption is projected to increase by 28% between 2015 and 2040, especially due to increased economic growth, access to marketed energy, and quickly growing populations in non-OECD countries (U.S. Energy Information Administration 2017), that outweigh increasingly energyefficient technologies. Thereby, in 2017, domestic and commercial building sectors’ combined contribution to U.S. energy consumption has reached 27% (Pe ´rez- Lombard, et al. 2008; U.S. Energy Information (Pe ´rez-Lombard, et al. 2008; U.S. Energy Information Administration 2015) and is projected to increase by 32% between 2015 and 2040, an increasing proportion of which is electricity consumption with an annual increase of 2% (U.S. Energy Information Administration 2017). Thus, for building operation, which has the objective to manage buildings and their facilities (e.g., technical infrastructure, heating, ventilation and air conditioning), volatile electricity prices are a difficult challenge and electricity demand management is an important task. Building operators can reduce their volatility-exacerbated electricity costs by utilizing flexibility in electricity consumption, which ‘‘bear[s] economic value’’ (Fridgen, et al. 2016, p.538). As electricity prices—depending on the market—are likely to be lower during some periods (e.g., night times), it is preferable to consume electricity in these periods rather than during periods, in which prices are regularly Business Research (2020) 13:1491–1525 1493 123 at their peak (e.g., noon). Following Rozali, et al. (2014, p.2464) load shifting (LS) defines the ‘‘process of reallocating the electricity demands from the peak periods when the electricity tariff is high, to off-peak periods when the electricity tariff is low’’. While LS is usually not possible for the entire electricity demand, already minor LS flexibilities can yield substantial electricity cost savings. More precisely, certain appliances are interactive and usually lack flexibility potential (e.g., television, lighting, stove, office equipment) (Barker, et al. 2012), however, other appliances may contain flexibility potential that can be utilized by smarter control systems (e.g., air conditioning systems, water boiler, washing machine). The research domain for utilizing LS flexibility is called demand response (DR). DR is defined as ‘‘changes in electric usage by end-use customers from their normal consumption patterns in response to changes in the price of electricity over time […]’’ (Federal Energy Regulatory Commission 2008, C-2). In U.S. building operation, a/c systems are an important influencing factor of electricity costs (U.S. Energy Information Administration 2016) and denote a subcategory of building automation systems, i.e., systems which are ‘‘widely employed in modern buildings to realize automatic monitoring and control of building services systems’’ (Liu, et al. 2009, p.1138). Nevertheless, to this day, there are many a/c systems that are manually controlled ((Ferreira, et al. 2012) and prone to run constantly throughout the day, even during disused hours on working days, weekends, and night times. These a/c systems possess LS flexibility potential by reducing a/c to the ondemand usage in advance to the occupancy of a room or building. Other a/c systems provide ‘‘automatic control of the indoor environment conditions’’ (Ducreux, et al. 2012, p.4847) and either preset a/c activation to a fixed time of day or trigger a/c activation by temperature measurements within the building’s sensor networks. Opposite to these approaches, the present paper aims to contribute to the development of data-driven decision support systems (DSS) that make a/c additionally cost-sensitive. In general, DSSs are ‘‘computer technology solutions that can be used to support complex decision making and problem-solving’’ (Shim, et al. 2002, p.111). According to the ‘‘Expanded DSS Framework’’ of (Power 2008, p.127), the special type of data-driven DSS ‘‘emphasizes access to and manipulation of a time series of internal […] data and sometimes external and real-time data’’. Data-driven DSSs can significantly improve electricity management for a/c systems by monitoring and processing decision-relevant information from different information sources. They can integrate both building-specific information (e.g., current and required inside temperature, occupancy schedules) and external information (e.g., historical and real-time electricity price information, weather information) to enable ex-ante optimal LS decision making. Compared to many existing approaches to building automation, these decisions are time-saving and cost-saving under consideration of human objectives and frame conditions. Hence, the present paper covers a relevant real-world problem: ‘‘How can data-driven decision support for load shifting reduce electricity costs in real estate air conditioning systems?’’ For the creation of data-driven DSSs, smart and machine supported information systems are of great value. An advanced metering infrastructure (AMI) as a subcategory of information and communication technology (ICT) records ‘‘customer 1494 Business Research (2020) 13:1491–1525 123 consumption (and possibly other parameters) hourly or more frequently and provides for daily or more frequent transmittal of measurements over a [bidirectional] communication network to a central collection point’’ (Federal Energy Regulatory Commission 2008, p.5). Therefore, AMI enables rapid information exchange and remote control for activating and deactivating a/c systems. A building operator’s LS decision on a/c depicts a dynamic and stochastic optimization problem. Therefore, this paper presents an artifact to address this realworld problem by following principles of the design science research (DSR) paradigm (Gregor and Hevner 2013; Hevner, et al. 2004; Peffers, et al. 2007). The artifact comprises a DR approach for data-driven DSSs, which enables building operators to perform real-time decision making on LS. The DR approach is embedded into a standardized four-step framework and decision making is realized by an algorithm that requires building operators to set a few input parameters. Thereby, the DR approach automatically searches for the expected optimal activation time of the a/c system within a specified temporal flexibility window. Three artifact requirements are postulate d: It must be easy to understand and use, without requiring engineering expertise or thermal modeling (i.e., simple). It must be applicable for a broad range of applications scenarios (i.e., general), and it must integrate electricity price and demand prediction (i.e., forward-looking). The paper is structured as follows: This section discusses the purpose and scope of the artifact and its relevance for the target audience (building operators). Section 2specifies the problem context in detail and presents findings from prior research. Section 3presents the artifact referred to as DR approach. Section 4 contains the artifact demonstration and a rigorous design evaluation that underpins the validity, utility, and quality of the artifact based on a real-world business case with historical data from two large public buildings. Section 5summarizes results and discusses limitations and possible future research. 2 Related work The development of an artifact, which enables building operators to reduce electricity costs using ICT-enabled decision support, is a contribution to energy informatics (EI). EI is concerned with ‘‘analyzing, designing, and implementing systems to increase the efficiency of energy demand and supply systems’’ (Watson, et al. 2010, p.24). An application domain of EI is demand-side management (DSM), which comprises ‘‘approaches such as the general increase in energy efficiency and time-based electricity pricing for end-consumers’’ (Feuerriegel and Neumann 2014, p.359). Strbac (2008) provides an overview of DSM, explaining both benefits and challenges. The author lists DSM as a means to reduce long-term electricity reserve, to reduce preventive measures for power system security, to improve operation efficiency, and to manage network constraints at the distribution level (Strbac 2008). DR is a subclass of DSM (Sui, et al. 2011), which is an umbrella term (Feuerriegel and Neumann 2014). DR is more customer-centric by promoting their interaction and responses to market signals (e.g., electricity prices) (Albadi and El-Saadany Business Research (2020) 13:1491–1525 1495 123 2008; Siano 2014; Palensky and Dietrich 2011). Fridgen et al. (2016) propose a DR valuation method for LS flexibility from a utility’s perspective using real option analysis. They build on prior research applying real option analysis (Benaroch and Kauffman 1999; Ronn 2002; Sezgen et al. 2007; Ullrich 2013) and develop a model to dynamically optimize LS in discrete time increments. For households and small businesses, Conejo et al. (2010) develop a model to dynamically adjust the hourly load level in response to consumption constraints and electricity prices, which are forecasted within confidence intervals. Lujano-Rojas et al. (2012) present an optimal DR load management strategy, which considers electricity price prediction, user-defined preferences on energy demand, renewable power production, and electric vehicle utilization. In two case studies, they illustrate that users of the proposed model can reduce electricity bills between 8 and 22%. Presenting a tool to maximize social welfare, Su and Kirschen (2009) illustrate that electricity prices tend to decline by increasing usage of LS. In a case study, Albadi and El-Saadany (2008) demonstrate that DR reduces electricity price peaks and changes the consumption patterns of endconsumers. The authors list the benefits of DR and find that savings are not only possible for participating customers, but for all customers in the market. Further, they find positive effects of DR on electricity system reliability and electricity market performance. Mohsenian-Rad, et al. (2010, p.329) use a game-theoretic approach to illustrate that in the presence of a real-time electricity market, each user has the incentive to participate in a scheduling game. They propose an ‘‘optimal, autonomous, and distributed incentive-based energy consumption scheduling algorithm’’ that aims to minimize ‘‘the cost of energy and also to balance the total residential load’’ (Mohsenian- Rad, et al. 2010, p.329). Further, they focus on communication among users rather than interactions between a utility company and its customers. For residential customers, Gottwalt, et al. (2011) build different scenarios with flat and time-based electricity tariffs. Without uncertainty in a day-ahead hourly pricing regime, households can realize significant savings in electricity costs. In the context of commercial building operation, Zhou, et al. (2011) build an agent-based simulation model and illustrate that DR actions by several building operators shave load profiles at peak hours (peak clipping), reduce the volatility of aggregated electricity demand, reduce electricity prices (and therefore electricity costs), and reduce electricity price volatility. Bahrami, et al. (2012) suggest a new load management strategy to reduce building operators’ electricity costs. Their DR approach models electricity prices as a convex function of electricity demand and supply, i.e., an individual building operator’s hourly market price is influenced by information about the total electricity consumption of all customers and the total generation capacity of the respective utility. However, since building operators usually lack such detailed market information, this approach is rather game theoretic and only applicable from a utility’s perspective. A model for electricity price prediction is developed by Mohsenian-Rad and Leon-Garcia (2010) who propose an automatic energy consumption scheduling framework. Similar to the present paper’s objectives, these authors intent to help building operators ‘‘to shape their response [to electricity prices] properly and in an automated fashion’’ (Mohsenian-Rad and Leon-Garcia 2010, p.121). While the present paper’s approach takes into account the dependence of electricity demand on temperature forecasts, Mohsenian-Rad and 1496 Business Research (2020) 13:1491–1525 123 Leon-Garcia (2010) require building operators to manually announce their upcoming electricity demand using AMI. Henze (2005) presents a model-based approach for predictive control of active and passive thermal storage inventory. Their supervisory controller includes short-term weather prediction and therefore a/c electricity demand prediction, time-of-use differentiated electricity prices, and real-time control strategies with dynamically updated forecasts. However, since these authors assume electricity rate structures to be visible and exogenously predetermined by the utility, their model is not suited for situations in which a building operator must decide based on real-time electricity market price information with stochastic future development. The present paper grasps this situation by applying a prediction methodology for intraday electricity price development under consideration of historical price patterns. Another approach that integrates dynamic electricity tariffs and electricity storage management is presented by Oldewurtel, et al. (2011). Like the present paper’s approach, these authors model dynamic electricity prices with stochastic future development to achieve electricity cost savings by exploiting LS flexibilities. Instead of predicting electricity demand, however, these authors empirically collect and aggregate historical demand profiles, which makes their model insensitive for individual electricity consumption. While these articles focus on a macrogrid perspective, there is also a substantial research stream on decentral microgrid concepts (e.g., Guerrero, et al. 2012; Hatziargyriou, et al. 2007;Mu ¨nsing, et al. 2017; Thiam 2010). Though this perspective is gaining increasing importance with the further distribution of decentralized (renewable) electricity generation (e.g., solar, wind), this manuscript focuses on the optimization of electricity costs in the presence of a macro electricity market (i.e., against public market prices). Most of the mentioned studies rely on data-driven decision making and assume smart grids and respective ICT (especially AMI) as technological enablers. Concluding, researchers have already started to develop data-driven DR approaches by suggesting new control logics in building operation, which might be part of future DSSs. The present paper strives to contribute to this development by addressing especially one identified research gap: To the best of the authors knowledge, formal DR approaches which dynamically predict electricity prices and electricity demand for a/c systems based on weather information and occupancy schedules and that perform automated and real-time decision support on LS with the objective to reduce electricity costs do not exist so far. 3 Artifact description In this section, the present paper continues to ‘‘create and evaluate(the appropriate) IT artifact intended to solve [the] identified problem’’ (Hevner, et al. 2004, p.77). In line with the EI framework introduced by Watson, et al. (2010), the artifact supports building operators using flow networks (AMI) and sensitized objects (a/c system) to smarter consume electricity. Hence, it addresses the problem of a ‘‘lack of information to enable and motivate economic and behaviorally driven solutions’’ (Watson, et al. 2010, p.24). Business Research (2020) 13:1491–1525 1497 123 3.1 Scenario introduction The present paper defines an ‘‘a/c system’’ as technology that building operators use to change temperature (i.e., heating, or cooling) inside a room or building. Although many authors use the term heating, ventilation and air conditioning systems, this paper applies a/c systems as a general term, which can comprise all these use cases. The a/c system is part of a greater information system that ‘‘ties together the various elements to provide a complete solution’’ (Watson, et al. 2010, p.27). In the following, the artifact’s application scenario is explained along with prerequisite assumptions and the four-step framework embedding the DR approach to reduce electricity costs. The application scenario is characterized as follows: A building operator must prepare appropriate temperature according to an exogenously specified room or building (in the following referred to as object) occupancy schedule (Fig. 1). Occupancy time is the time when the considered object is not empty. The required inside temperature (tempreq) needs to be achieved until occupancy starts (T), whereas inside temperature prior to occupancy may deviate. For the DR approach, the present paper focuses on the time span between the first possible starting time for a/c (t 0 ) and the latest possible starting time for a/c (t L ). The latter is necessary to guarantee tempreq until occupancy: tLis the latest point prior to T at which a/c activation ensures temp req until T. By finding the expectedly optimal point in time between t 0 and t L (i.e., the temporal flexibility window for LS) to activate the a/c system, building operators can minimize expected electricity costs. During each day, several subsequent, non-overlapping events can take place in one object. Assumption 1: Building operators can deduce t0and tLby analyzing the occupancy schedule and tempreq is constant for different object occupancies. The DR approach uses the end of one occupancy as t0to optimize a/c for subsequent occupancy (if an occupancy is the first on the day, t0could be the previous day). Hence, due to the previous occupancy, the object’s inside temperature in t0can be assumed to be tempreq. If a/c is deactivated in t0, the object’s inside temperature starts striving toward outside temperature due to thermal movement. Assumption 2: The object’s inside temperature in t0equals tempreq. Considering the first artifact requirement (‘‘easy to understand and use’’), the DR approach applies discrete-time optimization, which is less complex and demanding (for decision-makers and ICT) than continuous-time optimization. Fig. 1 Exemplary time scheme prior to occupancy time 1498 Business Research (2020) 13:1491–1525 123 implemented in a working system’’ (Hevner, et al. 2004, p.79). Afterward, the DR approach is evaluated with multiple simulations of random scenarios to demonstrate that the ‘‘artifact (generally) works and does what it is meant to do’’ (validity) (Gregor and Hevner 2013, p.351). For both, real-world data is applied. The object that serves for demonstration and evaluation is located in the southeastern part of the United States, in Georgia. Georgia is known for its subtropical climate, with humid summers and moderate winters. Especially during summer months (May to September), temperatures are comparatively high (between 15–31.7 C on average). During winter months (November to March), temperatures are on average above freezing point (between 0.6 C and 18.3 C). For research purposes at the University of Georgia, a/c data were collected from two University buildings. The rooms within the buildings are used as offices and for large meetings. Both buildings are partly open to the public. Using measuring points, different parameters were collected during a period ranging from January 2010 to December 2014. Collected parameters comprise inside temperature on a room level, outside temperature, and electricity consumption (kWh) for a/c usage. Measuring points recorded instantaneous, i.e., not as averaged values within a certain time span. Main components of the a/c system are two chiller systems that jointly air-condition via chilled water loops. Together, both chiller systems have a maximum wattage of 1.2 MW and are responsible for 90% of the a/c system’s total electricity consumption. The remaining 10% are consumed by auxiliary equipment that scales up with the chillers’ current load level. By applying variable load control, the a/c system is designed to provide a constant supply water temperature (about 5C?/-0.2 C). Electricity consumption of the a/c system depends on the temperature of return water (that, in turn, depends on outside and the buildings’ inside temperature). Warmer return water increases electricity consumption and vice versa. To date, no DR mechanism is in place and the (central) a/c system runs all day (not to be confused with a single room’s air supply, which can toggle on and off), even in times of low or no occupancy (e.g., on weekends and at night). Overall, the current system wastes energy and yields unnecessary electricity costs. The University purchases electricity for the a/c system from a local utility company. The company charges real-time electricity prices rather than offering a flat plan. Thus, electricity prices are sometimes high and the University incurs significant electricity costs. The collected data of the a/c system and payed electricity prices make this example suitable for the DR approach’s demonstration and evaluation. Although a data-driven DSS that integrates the DR approach is not implemented yet, its theoretical cost savings potential is evaluated in this scenario. For variable load control, the a/c system already possesses sensor systems that measure further parameters such as supply water temperature and current load level, a web server that collects all sensor information, and a remote controller that building operators can access using a web portal. Access to the utility’s real-time electricity prices is available using the customer portal. To establish cost-sensitive a/c control, there is a need for changes and enhancements in the monitoring and control system as it must dynamically import the utility’s price information (by accessing a respective application interface) and possess control software that applies the data-driven DR approach. Moreover, hardware for faster communication Business Research (2020) 13:1491–1525 1505 123 and computation would be useful in order that the system can react on changes in input information in near real-time (which is especially necessary to scale downtime increment length between two optimization iterations). Due to an expert’s opinion (an engineer at the university with a PhD who is specialized in a/c systems), the sum of all university-internal and -external costs for implementing such cost-sensitive control in the considered a/c system amounts to about $100.000. Further running costs are expected to be insignificantly low. Besides this application scenario, the expert expects the control software to be applicable in other university buildings as soon as they are also equipped with modern monitoring and control systems. To obtain a conservative estimate, the present paper limits the business case analyses to the described scenario. 4.2 Step 1 Scheduling (demonstration) For artifact demonstration, tempreq is set to 21 C. This is the currently targeted inside temperature in the scenario’s buildings. As Georgia, USA, is known for its humid and hot summers, a typical day in September is chosen, when a/c is required to cool (keep) the inside temperature to (at) 21 C. In particular, the DR approach is applied on September 04, 2014. The hypothetical event of interest (e.g., a major event of a university initiative) takes place at 2 pm (occupancy time) in both buildings. The earliest possible a/c activation is set to 7am. The University’s expert stated that every room within the two buildings (regardless of current inside and outside temperature) can be cooled down to tempreq by a/c within one hour. Hence, tLis at 1 pm (i.e., x¼1). As the dataset of historically payed electricity prices features hourly time increments, artifact demonstration and evaluation is also conducted with hourly time increments between t0and tL. Table 1illustrates the schedule. 4.3 Step 2a: Price prediction (demonstration) As described in Sect. 3, this paper modifies and applies the price prediction model developed by Fridgen, et al. (2016). This price prediction model draws upon the existence of historical time of day- and season-specific price patterns and updates price prediction at every time step by integrating new observable price information. Figure 7illustrates historical time of day-specific price patterns of electricity prices. Further, Table 2illustrates descriptive statistics on electricity price patterns of different months. Table 1 Schedule for artifact demonstration Time t 0 = 7am 8am 9am 10am 11am 12noon t L =1pm T=2pm t012345 6 7 1506 Business Research (2020) 13:1491–1525 123 For configuration purposes, building operators can adjust three endogenous (model) parameters within the DR approach’s price prediction model: h,n(the adjustment reference interval to compute shot-term adjustment a), and an estimation corridor to compute  SðtÞ. Fridgen, et al. (2016) vary hwithin an interval between 0 and 1. For artifact demonstration, his arbitrarily set to 0.8 and further analysis of its influence is left to the subsequent evaluation. Similar, n is set to 0. To calculate  SðtÞ, Fridgen, et al. (2016) analyze seasonal price patterns. The authors differentiate Table 2 Descriptive statistics for electricity prices per month [$/kWh] Mean Std. Dev Min Max January 0.06983 0.03770 0.04693 0.67465 February 0.06509 0.00937 0.04756 0.11991 March 0.06392 0.00784 0.03000 0.10256 April 0.06467 0.00929 0.04483 0.11846 May 0.06460 0.00916 0.04661 0.17592 June 0.07623 0.03772 0.04643 0.42924 July 0.08093 0.04634 0.04800 0.41529 August 0.08153 0.05391 0.04981 0.72466 September 0.06680 0.01556 0.04919 0.31288 October 0.06406 0.00828 0.04600 0.09350 November 0.06387 0.00864 0.05137 0.30558 December 0.06414 0.01241 0.04933 0.36954 Fig. 7 Hourly mean electricity prices (June 2012–November 2014) Business Research (2020) 13:1491–1525 1507 123 between summer, winter, and intermediate season. However, this does not fully reflect the course of historical time-of-day-specific price patterns. For example, their intermediate seasons include March–May and September–November. Therefore, March and September share the same  SðtÞ, which is (in our case) not accurate as shown in Table 2. Hence, this paper calculates  SðtÞbased on a historical corridor around the date of interest and time-of-day. For the presented example (September 04, 2014),  SðtÞat (e.g.) 12 noon is calculated by averaging previous-years’ historical electricity prices from (e.g.) 30 days prior to 30 days after the date of interest, i.e., from August 05, (2010–2013) to October 04, (2010–2013) each of which at 12 noon. Table 3illustrates respective results (with SðtÞbeing the actual observable electricity prices). Example: ES9amj8amðÞðÞ¼ EðS8amj8amðÞþhða8amðÞ S9amðÞ ES8amj8amðÞðÞ¼0:0599 þ0:80:95520:0625 0:0599ðÞ¼0:0597 In the next step, the DR approach estimates Dðti;1pm;1Þ. As described in Sect. 3.5,Dðti;1pm;1Þis split into ID ti;1ðÞand PD ti;1pm;1ðÞ(as x¼1 is constant within the real-world scenario, this section continues with a reduced formal notation that neglects x). For the real-world scenario, Table 4illustrates related DtemperatureðtÞand PDðtÞobservations and a respective linear regression. The real-world scenario’s a/c system is intended for cooling only. Cooling for DtemperatureðtÞ\0 implies that the two buildings were still heated up when outside temperature already fell below tempreq. Unfortunately, historical temperature forecasts that match the given historical data set were not obtainable. Hence, for artifact demonstration and evaluation, this paper requires an assumption to predict electricity demand: 4.4 Actual outside temperature equals previous weather forecasts Generally, Assumption 4 depicts a great simplification of reality. However, since the DR approach focusses on short-term schedules for only a few hours, weather forecasts are close to reality (National Weather Service 2017). Moreover, subsequent evaluation integrates an artificial demand prediction error to analyze electricity cost savings’ sensitivity to demand forecasting quality. Hence, the algorithm can use historical outside temperature as previous weather forecasts to compute PDðtÞ. Table 5illustrates the respective results. To date, historically collected parameters are only appropriate for the estimation ofPDðtÞ. To precisely estimateIDðtiÞ, experimental runs would be necessary that analyze different a/c deactivation durations and different outside temperature developments. However, these experimental runs have not been conducted yet. As interim solution, threshold values are applied that logically contain the correctIDðtiÞ. For the lower limit applies:IDðtiÞ¼0, i.e., a situation in which no a/c is required to restoretempreq. For the upper limit applies: ID ti ðÞ¼ P t¼ti1 t¼t0 PD tðÞ, which equals the sum of all electricity that would have been necessary to keep the inside 1508 Business Research (2020) 13:1491–1525 123 temperature at tempreq at any time sincet0. Until more accurate solutions or historical data are available, IDðtiÞ2½0;Pt¼ti1 t¼t0PDðtÞ is an appropriate interval to estimateIDðtiÞ. For demonstration, we arbitrarily choose a parametere¼0:4, which Table 3 Price prediction parameters (i) Time (September 04, 2014) 7am 8am 9am 10am 11am 12noon 1 pm 2 pm (ii) i 012345 67 (iii)  SðtÞ[$] 0.0585 0.0608 0.0625 0.0643 0.0671 0.0732 0.0833 0.0959 (iv) SðtÞ[$] 0.0606 0.0599 0.0639 0.0655 0.0676 0.0692 0.0708 0.0906 (v) h0.8 (vi) a(n¼0)0.9457 0.9552 0.9712 1.0227 1.0351 0.9855 1.0212 1.0178 Expected electricity price Sðtjt0Þat time t in [$/kWh] (utilizing F-1) (vii) EðSðtj7amÞÞ 0.0606 0.0581 0.0594 0.0618 0.0673 0.0741 0.0805 0.0944 (viii) E S tj8amðÞðÞ 0.0599 0.0597 0.0619 0.0673 0.0741 0.0805 0.0944 (ix) E S tj9amðÞðÞ 0.0639 0.0627 0.0674 0.0741 0.0805 0.0944 (x) E S tj10amðÞðÞ 0.0655 0.0680 0.0742 0.0805 0.0944 (xi) E S tj11amðÞðÞ 0.0676 0.0741 0.0805 0.0944 (x) E S tj12noon ðÞðÞ 0.0692 0.0795 0.0943 (xi) E S tj1pmðÞðÞ 0.0708 0.0925 (xii) E S tj2pmðÞðÞ 0.0906 Table 4 Empirical dependence of PD tion Dtemperature Model parameters PDti *Dtemperatureti Estimate Standard error t-value Pr( [|t|) Intercept 428.5889 1.1151 384.3 2e-16 *** Dtemperatureti21.8235 0.1775 122.9 2e-16 *** Significance codes 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘’ 1 Multiple R-squared 0.5645 Adjusted R-squared 0.5644 F-statistic 1.511e ?04 Pvalue 2.2e-16 Business Research (2020) 13:1491–1525 1509 123 simulates a building that absorbs heat to a medium extent. Table 5(vii) illustrates estimations for IDðtiÞand (viii) estimations for D ti;12ðÞ: ID ti ðÞ¼eX t¼ti1 t¼t0 PD tðÞ ð7Þ Example: PD 8amj2pmðÞ¼InterceptDtemperature tðÞEstimate ¼428:5889 þ 3:821:8235 PD 8amj2pmðÞ¼ PD 8amðÞ 2þP t¼ 9am2pmPD tðÞ¼511:52 2þ520:25 þ...þ697:02 þ 550:80 ID 1pmðÞ¼eP t¼ti1 t¼t0 PD tðÞ¼0:4507:15 þ511:52 þ...ðÞ¼1411:83 Table 5 Development of DtemperatureðtÞand PD (t) (i) Time (September 04, 2014) 7am 8am 1 pm 2 pm (ii) t 0167 (iii) Outside Temperature (t) [C] 24.6 24.8 33.3 26.6 (iv) DtemperatureðtÞ[K] 3.6 3.8 12.3 5.6 (v) PDðtÞ[kwh] 507.15 511.52 697.02 550.80 (vi) PDðti;2pmÞ[kwh] (utilizing F-3) 4523.83 4014.49 899.31 275.40 (vii) IDðtiÞ[kwh] (utilizing F-7) 0.00 202.86 1411.83 1690.64 (viii) Dðti;2pmÞ[kwh] (utilizing F-2,3) 4523.83 4217.35 2311.14 1966.04 Table 6 Decision making within artifact demonstration (i) Time (September 04, 2014) 7am 8am 10am 1 pm 2 pm (ii) SðtÞ[$/kWh] 0.0606 0.0599 0.0708 0.0906 (iii) E S tj7amðÞðÞ[$/kWh] 0.0606 0.0581 0.0805 0.0944 (iv) E S tj8amðÞðÞ[$/kWh] 0.0599 0.0805 0.0944 (v) E S tj1pmðÞðÞ[$/kWh] 0.0708 0.0925 (vi) IDðtiÞ[kwh] 0.00 202.86 1411.83 1690.64 (vii) PDðtÞ[kwh] 507.15 511.52 697.02 550.80 (viii) PDðti;2pmÞ[kwh] 4523.83 4014.49 899.31 275.40 Expected electricity costs at time t in [$] (utilizing F-5,6) (xi) EðCt i;2pmj7amðÞÞ 319.42 300.98 193.71 185.68 (xii) EðCðti;2pmj8amÞ)302.02 193.71 185.68 (xiii) EðCðti;2pmj1pmÞ)175.58 181.87 (xiv) EðCðti;2pmj2pmÞ)178.13 1510 Business Research (2020) 13:1491–1525 123 D1pm;2pmðÞ¼ID 1pmðÞþ PD 1pmðÞ 2þ P 2pm t¼2pm PD tðÞ¼1411:83 þ697:02 2þ550:80 ¼2311:14 4.5 4.5 Step 3: Decision making (demonstration) In the third step, the decision algorithm for LS determines if immediate a/c activation is ex-ante optimal (cost minimal). In particular, from the perspective of the current period, the algorithm predicts and compares expected total electricity costs for all possible activation periods. Table 6illustrates computations from the perspectives of 7a.m., 8a.m., 1p.m., and 2p.m. In this example, the algorithm would wait until 1 pm to initialize a/c. Example: EC1pm;2pmj1pmðÞðÞ¼ID 1pmðÞEðS1pmj2pmðÞþ P 2pm t¼1pm EStj2pmðÞPD tðÞðÞ¼0:07081411:83 þ0:0708697:02 2þ:0925550:80 ¼175:58 4.6 4.6 Step 4: Feedback (demonstration) In the last step, the DR approach ex-post evaluates the ex-ante chosen activation time as described in Sect. 3.7. Therefore, the DR approach computes savings of its decision compared to the default procedure with no DR. By applying DR and activating a/c at 1 pm, total electricity costs would have been $174.53 (cf. Table 7ii). These are the lowest actual (not expected) total costs and can be computed by utilizing F-6 with the actual (not expected) electricity prices. The default procedure, however, would have yielded total electricity costs of $312.90 (cf. Table 7ii). This equals an electricity cost reduction of 44.22% due to the DR approach. Moreover, the theoretically optimal point in time for a/c activation (the benchmark) was also at 1 pm. In particular, the DR approach was able to utilize the entire cost savings potential. Table 7summarizes the results for the presented example. Cexpost are calculated using the demand for each hour and according actual prices, not the expected prices. Since this example is biased in its validity because it was manually picked, the next section contains randomly chosen historical simulations and sensitivity analysis. Thereby, the general usefulness of the artifact is analyzed. 4.7 Evaluation DSR methodology calls for an evaluation of a developed artifact to provide evidence ‘‘how well the artifact supports a solution to the problem ‘‘(Peffers, et al. 2007, p.56). A possible evaluation method within DSR are simulations (Hevner, et al. 2004). This paper’s evaluation is divided into three parts and presents historical simulations on the real-world scenario with 200,000 simulation runs each: The first part gives an impression on the DR approach’s effectiveness in terms of average electricity cost savings and sensitivity of the latter to endogenous model Business Research (2020) 13:1491–1525 1511 123 Table 7 Decision making within artifact demonstration (i) Time (September 04, 2014) 7am 8am 9am 10am 11am 12am 1 pm 2 pm (ii) Cexpostðti;2pmÞ[$] 312.90 294.36 276.31 253.93 228.61 201.76 174.53 178.12 (iii) Cexpost;realizedðti;2pmÞ[$] 174.53 (iv) Cexpost;default[$] 312.90 (v) Cexpost;Benchmark[$] 174.53 (vi) Realized cost savings [%] 44.22% (vii) Savings potential exploitation [%] 100% 1512 Business Research (2020) 13:1491–1525 123 parameters (h,n, and estimation corridor, c.f. Section 4.3). Subsequently, the triple of endogenous model parameters that yields the highest average electricity cost savings is fixed for the second part of the historical simulation. This calibration procedure for the prediction model is valid, as building operators can individually chose model parameters. The electricity cost savings of the second part are then analyzed on their sensitivity to exogenous scenario parameters (t0,tL, flexibility window lengthtLt0, and dependency of IDtion PDt). To lift Assumption 4, a third simulation part integrates an artificial hourly demand prediction error). Therefore, the sensitivity of electricity cost savings to forecasting quality of electricity demand is measured. For all simulation parts, sensitivity of the results to the electricity market is analyzed by also repeating every simulation with electricity prices from the German-Austrian market area of EPEX SPOT. This market has a significantly growing capacity of renewable energy generation (EPEX SPOT 2017) that may evolve to a global trend. To isolate market influences on the results, the object and temperature conditions are assumed to equal the real-world scenario. In the following, this section refers to both markets as US market and EU market, respectively. Results of all simulation parts are discussed afterward. 4.7.1 Historical simulation – part 1 A multivariate sensitivity analysis identifies the triple of all three endogenous model parameters that yield (in combination) the highest average electricity cost savings: h¼1:0, n¼6h, and estimation corridor length ¼30 days with average electricity cost savings of $99.76 (or 45.40%) for the US market and h¼1:0, n¼0h, and estimation corridor length ¼60 days with average electricity cost savings of €51.28 (or 46.11%) for the EU market. As building operators can individually select endogenous model parameters, they should always conduct such pre-simulations on their individual historical data to maximize electricity cost savings. Thereby, as the present example illustrates, the best parameter combination can vary between Table 8 Range of evaluation parameters (Simulation—part 1) Parameter Values (intervals) Simulation runs 200,000 Date {June 01, 2012,…,November 30, 2014} Randomized Starting time t0{6am,7am,…,6 pm} Randomized Latest point for a/c activation tL{(t0þ1),…,min(10 pm, (t0þ8))} Randomized Theta h{0, 0.25, 0.5, 0.75, 1.0} Randomized Reference interval n[h] {0, 2, 4, 6 no a} Randomized Estimation corridor for  S[days] {30, 60, 90} Randomized Initial Demand IDðtiÞ[kwh] f0;0:25 Pt¼ti u0PDðtiÞ;...;1:0Pt¼ti t¼t0PDðtiÞg Randomized Business Research (2020) 13:1491–1525 1513 123 different electricity markets. In the second part of the simulation, the respective best parameter combinations are fixed for both markets. 4.7.2 Historical simulation–part 2 Table 8illustrates the evaluation parameters and their range. Simulation runs are conducted by sampling with replacement. Overall parameter combinations, the DR approach yields average electricity cost savings of $94.61 (or 44.52%) for the US market and €48.42 (or 44.07%) for the EU market compared to the default procedure with no DR. Standard deviation is $134.62 (142.29% of mean) for the US market and €52.30 (108.01% of mean) for the EU market. The cost savings potential (i.e., the benchmark) is $99.63 (or 46.88%) for the US market and €50.58 (or 46.03%) for the EU market. Therefore, the utilization of cost savings potential by Table 9 Sensitivity of absolute and relative savings to endogenous (model) parameters US market EU market Absolute savings Relative savings Absolute savings Relative savings Mean-reversion h 0 $92.36 43.47% €48.25 43.80% 0.25 $93.41 44.19% €47.90 43.77% 0.5 $95.45 44.78% €48.37 43.98% 0.75 $95.62 44.96% €48.66 44.37% 1 $96.22 45.19% €48.94 44.41% Two-Sample t-Test: Reject H0hypothesis for both markets (US ***, EU ***) that mean savings of ( h\0:5)mean savings of ( h0:5Þ, consequently higher hpreferable Adjustment reference interval n 0 h $96.23 45.23% €49.12 44.71% 2 h $95.13 44.61% €48.83 44.45% 4 h $93.91 44.16% €48.48 44.11% 6 h $93.38 44.06% €48.24 44.01% Off $94.41 44.54% €47.46 43.07% Two-Sample t-Test: Reject H0hypothesis for European market (US -, EU ***) that mean savings of ‘‘short-term adjustment’’ mean savings of ‘‘no short-term adjustment’’, consequently applying short-term adjustment preferable Two-Sample t-Test: Reject H0hypothesis for both markets (US ***, EU ***) that mean savings of ( n6¼ 0)mean savings of ( n ¼0Þ, consequently n ¼0preferable Estimation corridor length 30 $94.47 44.54% €48.29 44.07% 60 $95.02 44.65% €48.39 44.00% 90 $94.34 44.37% €48.58 44.13% Two-Sample t Tests: No significant preferences for both markets (US -, EU -) *** Significant for 1% level, ** significant for 5% level, * significant for 10% level 1514 Business Research (2020) 13:1491–1525 123 5 Implications, limitations, and further research 5.1 Implications The present research contributes to the development of data-driven DSSs that can significantly reduce building operators’ electricity costs. In particular, a DR approach is presented, which utilizes existing LS flexibility potential of a/c systems by performing real-time decision making. The latter requires rapid information exchange and remote control for activating and deactivating a/c, which is enabled using modern ICT (especially AMI). The DR approach satisfies the requirements stated in the introduction: It is simple, general, and forward-looking. Computations are feasible without engineering expertise because they focus on data-driven decision making. Building operators can use the presented four-step framework to derive their individual DR approach for real-estate a/c systems. The development of the DR approach follows the principles of the DSR Paradigm. The artifact demonstration and evaluation propose that the DR approach is valid (‘‘validity’’) (Gregor and Hevner 2013). By applying real-world data from two university buildings and a respective business case, the present paper demonstrates the usability of the artifact in practice (‘‘utility’’) (Hevner, et al. 2004). Within the real-world scenario, the artifact would be able to yield remarkable electricity cost savings compared to current existing a/c procedure (‘‘quality’’) (Gregor and Hevner 2013). However, sensitivity analysis illustrate that the payback period of the real-world business case does strongly depend on endogenous model and exogenous scenario parameters. Within similar frame conditions, the developed artifact provides considerable electricity cost savings between 42 and 45% across the German and a special US electricity market. The tested parameters illustrate that mean-reversion parameter, adjustment reference interval, and estimation corridor length tend to influence electricity cost savings. Moreover, this research paper suggests how to analyze the business case for implementing and running cost-sensitive a/c control (using the DR approach). Our results imply that the viability of such an investment depends critically on the frequency of LS measures and the extent of possible electricity cost savings. 5.2 Limitations and further research There are also limitations to the DR approach. First, an assumption is made that actual outside temperature equals previous temperature forecasts (i.e., there is no uncertainty in electricity demand). Indeed, weather forecasts for only a few hours are close to reality (National Weather Service 2017), which is confirmed by this paper as it additionally applies an additional sensitivity analysis, which implements an artificial hourly demand prediction error that proves to have only little influences on results. However, future research should further develop the presented approach and waive this simplification. Second, this paper assumes a constant required room temperature tempreq and, therefore, focuses on temporal flexibility of a/c systems. Business Research (2020) 13:1491–1525 1521 123 However, we neglect the possibility to generate further cost savings by considering flexibility in quality (i.e., flexibility of tempreq), which would be a promising extension for future research. Third, since authors have no data to estimate the dependence of initial a/c electricity demand on the previous hours’ outside temperature development, only an interim solution is applied that basis on interval estimation. Especially an application in practice or the cooperation with other fields of research would yield important insights to further specify the quantification of initial a/c electricity demand. Fourth, the DR approach is limited to only one procedure of performing a/c. In particular, for reasons of simplicity, it cannot account for scenarios in which a building operator dynamically activates and deactivates the a/c system. A procedure that allows at each discrete time step to either activate or deactivate a/c and (for a/c activation) to control a/c intensity should further increase the cost savings potential. Fifth, there is also a proportion of simulation runs, in which cost savings are negative. To strengthen confidence, trust, and attention into DR technologies, future research should try to develop DR approaches that reduce the occasions of negative results. As negative results are more formative (Rozin and Royzman 2001), this might deter building operators to apply DR (Venkatesh, et al. 2003). Nevertheless, the designed artifact is a robust data-driven method for building operators and can be used beyond the application domain. By its simplicity, generality, and forward-look, it depicts a suitable solution for many applicants. In line with Palensky and Dietrich (2011), this is also a further step to make DSM more customer-centric in the future. Sixth, besides presented approaches for electricity price and demand prediction, future research could apply and compare other common modeling approaches such as Holt-Winters seasonal models (Holt 2004; Winters 1960) for electricity price prediction or consumptionbased asset pricing models (Breeden 1979) for electricity demand prediction. Finally, we consider macrogrids as the only source for electricity. In times of increasingly decentralized power generation, e.g., by local solar modules on building roofs, integrated approaches of ‘‘make-or-buy-electricity’’ should be worth consideration. Therefore, future research could grasp our approach, e.g., to build an algorithm that decides in discrete time increments to either sell self-generated electricity or use it for premature a/c before room occupation. Acknowledgements This research was (in part) carried out in the context of the Project Group Business and Information Systems Engineering of the Fraunhofer Institute for Applied Information Technology FIT. Furthermore, the authors would like to thank Richard T. Watson and Thomas Lawrence for their support, their data collection, data provision, and helpful advice. Finally, the authors gratefully acknowledge the financial support of the Kopernikus-project ‘‘SynErgie’’ by the Federal Ministry of Education and Research (BMBF) and the project supervision by the project management organization Projekttra ¨ger Ju ¨lich (PtJ). Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain 1522 Business Research (2020) 13:1491–1525 123 permission directly from the copyright holder. To view a copy of this licence, visit http:// creativecommons.org/licenses/by/4.0/. References Albadi, M.H., and E.F. El-Saadany. 2008. A summary of demand response in electricity markets. Electric Power Syst Res 78 (11): 1989–1996. Bahrami S, Parniani M, Vafaeimehr A. (2012) A modified approach for residential load scheduling using smart meters. In: 3rd IEEE PES Innovative Smart Grid Technologies Europe Benaroch, Michel, and Robert J. Kauffman. 1999. A case for using real options pricing analysis to evaluate information technology project investments. Inform Syst Res 10 (1): 70–86. Breeden, D.T. 1979. An intertemporal asset pricing model with stochastic consumption and investment opportunities. Journal of Financial Economics 7 (3): 265–296. Conejo, Antonio J., Juan M. Morales, and Luis Baringo. 2010. Real-time dmand response model. IEEE Trans Smart Grid 1 (3): 236–242. Ducreux, L.F., C. Guyon-Gardeux, S. Lesecq, F. Pacull, and S.R. Thior. 2012. Resource-based middleware in the context of heterogeneous building automation systems. Ann Conf IEEE Electron Soc 2: 18–45. EPEX SPOT. 2017. Renewable energy: Increasingly important role in Europe. https://www.epexspot. com/en/renewables. (Accessed 05 July 2017). Federal Energy Regulatory Commission. (2008) Reports on Demand Response and Advanced Metering. https://www.ferc.gov/legal/staff-reports/12–08-demand-response.pdf. (Accessed 25 August 2015). Ferreira, P.M., A.E. Ruano, S. Silva, and E.Z.E. Conceicao. 2012. Neural networks based predictive control for thermal comfort and energy savings in public buildings. Energy Build 55: 238–251. Feuerriegel, Stefan, and Dirk Neumann. 2014. Measuring the financial impact of demand response for electricity retailers. Energy Policy 65: 359–368. Fridgen, Gilbert, Lukas Ha ¨fner, Christian Ko ¨nig, and Thomas Sachs. 2016. Providing utility to utilities: the value of information systems enabled flexibility in electricity consumption. J Assoc Inform Syst 17 (8): 537–563. Goebel, Christoph. 2013. On the business value of ICT-controlled plug-in electric vehicle charging in California. Energy Policy 53: 1–10. Gottwalt, Sebastian, Wolfgang Ketter, Carsten Block, John Collins, and Christof Weinhardt. 2011. Demand side management—a simulation of household behavior under variable prices. Energy Policy 39 (12): 8163–8174. Gregor, Shirley, and Alan R. Hevner. 2013. Positioning and presenting design science research for maximum impact. Manage Inform Syst Q 37 (2): 337–356. Guerrero, Josep M., Mukul Chandorkar, Tzung-Lin Lee, and Poh C. Loh. 2012. Advanced control architectures for intelligent microgrids—Part I: decentralized and hierarchical control. IEEE Trans Industrial Electron 60 (4): 1254–1262. Hatziargyriou, Nikos, Hiroshi Asano, Reza Iravani, and Chris Marnay. 2007. Microgrids. IEEE Power Energy Mag 5 (4): 78–94. Henze, Gregor P. 2005. Energy and cost minimal control of active and passive building thermal storage inventory. J Solar Energy Eng 127 (3): 343–351. Hevner, Alan R., Salvatore T. March, Jinsoo Park, and Sudha Ram. 2004. Design science in information systems research. Manage Inform Syst Q 28 (1): 75–105. Holt, Charles C. 2004. Forecasting seasonals and trends by exponentially weighted moving averages. Internat J Forecast 20 (1): 5–10. Illerhaus, S.W., and J.F. Verstege. 2000. Assessing industrial load management in liberalized energy markets. Power Eng Soc Summer Meeting 3: 18. Ketterer, Janina C. 2014. The impact of wind power generation on the electricity price in Germany. Energy Econ 44: 270–280. Liu, D., Z. Xu, Q. Shi, and J. Zhou. 2009. Fuzzy Immune PID Temperature Control of HVAC Systems, 1138–1144. Springer, Berlin, Heidelberg: International Symposium on Neural Networks. Business Research (2020) 13:1491–1525 1523 123 Ludig, Sylvie, Markus Haller, Eva Schmid, and Nico Bauer. 2011. Fluctuating renewables in a long-term climate change mitigation strategy. Energy 36 (11): 6674–6685. Lujano-Rojas, Juan M., Cla ´udio Monteiro, Rodolfo Dufo-Lo ´pez, and Jose ´L. Bernal-Agustı ´n. 2012. Optimum residential load management strategy for real time pricing demand response programs. Energy Policy 45: 671–679. Mohsenian-Rad, Amir-Hamed, and Alberto Leon-Garcia. 2010. Optimal residential load control with price prediction in real-time electricity pricing environments. IEEE Trans Smart Grid 1 (2): 120–133. Mohsenian-Rad, Amir-Hamed, Vincent W.S. Wong, Juri Jatskevich, Robert Schober, and Alberto Leon- Garcia. 2010. Autonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid. IEEE Trans Smart Grid 1 (3): 320–331. Mukherji, Sandip. 2011. The capital asset pricing model’s risk-free rate. Internat J Bus Financ Res 5 (2): 75–83. Mu ¨nsing E, Mather J, Moura S (2017). Blockchains for decentralized optimization of energy resources in microgrid networks. 2017 IEEE Conference on Control Technology and Applications (CCTA), Mauna Lani, HI, USA National Weather Service (2017). National Digital Forecast Database. https://www.ncdc.noaa.gov/dataaccess/model-data/model-datasets/national-digital-forecast-database-ndfd. Accessed on 14 June 2020 Oldewurtel, F., A. Ulbig, M. Morari, and G. Andersson. 2011. Building control and storage management with dynamic tariffs for shaping demand response. USA: Innovative Smart Grid Technologies IEEE. Palensky, Peter, and Dietmar Dietrich. 2011. Demand side management: demand response, intelligent energy systems, and smart loads. IEEE Trans Industrial Inform 7 (3): 381–388. Peffers, Ken, Tuure Tuunanen, Marcus A. Rothenberger, and Samir Chatterjee. 2007. A design science research methodology for information systems research. J Manage Inform Syst 24 (3): 45–77. Pe ´rez-Lombard, Luis, Jose ´Ortiz, and Christine Pout. 2008. A review on buildings energy consumption information. Energy Build 40 (3): 394–398. Power, Daniel J. 2008. Understanding data-driven decision support systems. Inform Syst Manage 25 (2): 149–154. Ronn, EhudI (ed.). 2002. Real options an energy management: using options methodology to enhance capital budgeting decisions. London: Risk Books. Rozali, Nor E.M., Sharifah R.W. Alwi, Zainuddin A. Manan, Jir ˇı ´J. Klemes ˇ, and Mohammad Y. Hassan. 2014. Cost-effective load shifting for hybrid power systems using power pinch analysis. Energy Procedia 61: 2464–2468. Rozin, Paul, and Edward B. Royzman. 2001. Negativity bias, negativity dominance, and contagion. Person Soc Psychol Rev 5 (4): 296–320. Sean, Barker, Aditya Mishra, David Irwin, Prashant Shenoy, and Jeannie Albrecht. 2012. SmartCap: Flattening peak electricity demand in smart homes. IEEE Internat Confer Perv Comput Commun 1: 67–75. Sezgen, Osman, C.A. Goldman, and P. Krishnarao. 2007. Option value of electricity demand response. Energy 32 (2): 108–119. Shim, J.P., Merrill Warkentin, James F. Courtney, Daniel J. Power, Ramesh Sharda, and Christer Carlsson. 2002. Past, present, and future of decision support technology. Dec Sup Syst 33 (2): 111–126. Siano, Pierluigi. 2014. Demand response and smart grids—a survey. Renew Sust Energy Rev 30: 461–478. Smith, J.C., Stephen Beuning, Henry Durrwachter, Erik Ela, David Hawkins, Brendan Kirby, Warren Lasher, Jonathan Lowell, Kevin Porter, Ken Schuyler, and Paul Sotkiewicz. 2010. Impact of variable renewable energy on US electricity markets. USA: IEEE Power and Energy Society General Meeting. Strbac, Goran. 2008. Demand side management: Benefits and challenges. Energy Policy 36 (12): 4419–4426. Strueker, Jens, Clemens Dinther (2012). Demand response in smart grids: research opportunities for the IS discipline. Proceedings of the American Conference for Information Systems. Su, Chua-Liang, and D. Kirschen. 2009. Quantifying the Effect of Demand Response on Electricity Markets. IEEE Transactions on Power Systems 24 (3): 1199–1207. Thiam, Djiby-Racine. 2010. Renewable decentralized in developing countries: appraisal from microgrids project in Senegal. Renew Energy 35 (8): 1615–1623. 1524 Business Research (2020) 13:1491–1525 123 U.S. Department of Treasury. 2017. Daily Treasury Yield Curve Rates Internet source, https://www. treasury.gov/resource-center/data-chart-center/interest-rates/Pages/TextView.aspx?data=yield. Accessed on 22 Nov 2020. U.S. Energy Information Administration. 2015. Annual Energy Outlook 2015. Wachington, DC: U.S. Energy Information Administration. U.S. Energy Information Administration. 2016. Annual Energy Outlook 2016: With Projections to 2040. Energy Information Administration: U.S. U.S. Energy Information Administration. 2017. International Energy Outlook 2017. Energy Information Administration: U.S. U.S. Energy Information Administration. 2018. Annual Energy Outlook 2018. Wachington, DC: U.S. Energy Information Administration. Ullrich, Christian. 2013. Valuation of IT investments using real options theory. Bus Inform Syst Eng 5 (5): 331–341. Venkatesh, Viswanath, Michael G. Morris, Gordon B. Davis, and Fred D. Davis. 2003. User acceptance of information technology: toward a unified view. Managet Inform Syst Q 27 (3): 425–478. Watson, Richard T., Marie-Claude Boudreau, and Adela J. Chen. 2010. Information systems and environmentally sustainable development: energy informatics and new directions for the IS community. Manage Inform Syst Q 34 (1): 4. Winters, Peter R. 1960. Forecasting sales by exponentially weighted moving averages. Management Science 6 (3): 324–342. World Economic Forum. 2017. Global Energy Architecture Performance Index Report. Cologny/Geneva, World Economic Forum: Switzerland. Zhou, Zhi, Fei Zhao, and Jianhui Wang. 2011. Agent-based electricity market simulation with demand response from commercial buildings. IEEE Trans Smart Grid 2 (4): 580–588. Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Business Research (2020) 13:1491–1525 1525 123