A Collection of 85 Datasets of Buildings and Building Clusters Performing Demand Response – Common Exercise IEA EBC Annex 82

A Collection of 85 Datasets of Buildings and Building Clusters Performing Demand Response – Common Exercise IEA EBC Annex 82 Hicham Johra Jérôme Le Dréau Michaël Kummert Alessia Arteconi Gregor Henze Alice Mugnini Megi Busho Ali Saberi Derakhtenjani Rahmat Heidari Tuğçin Kırant-Mitic Andrea Petrucci Zixin Jiang Bing Dong Jeeventh Kubenthiran Vojtěch Zavřel Rawad El Kontar Ben Polly Tim Diller Charles D. Corbin Table of Contents Table of Contents .............................................................................................................................................................. 2 Foreword ........................................................................................................................................................................... 3 Description of the collection of datasets .......................................................................................................................... 4 General information...................................................................................................................................................... 4 Detailed information ..................................................................................................................................................... 4 Dataset naming conventions ........................................................................................................................................ 7 Structure of the collection of datasets on demand response from buildings and clusters of buildings ...................... 8 Overview of the dataset cases .................................................................................................................................... 10 Foreword This report provides information about an open-access dataset containing 85 simulation datasets of buildings and clusters of buildings performing demand response. These datasets have been generated within the framework of a common exercise of the IEA EBC Annex 82 project: Energy Flexible Buildings Towards Resilient Low Carbon Energy Systems (https://annex82.iea-ebc.org/). For questions and comments, please contact Hicham Johra: h[email protected]. Description of the collection of datasets General information This open-access dataset contains 85 simulation datasets of buildings and clusters of buildings performing demand response. These datasets have been generated within the framework of a common exercise of the IEA EBC Annex 82 project: Energy Flexible Buildings Towards Resilient Low Carbon Energy Systems (https://annex82.iea-ebc.org/). Each dataset is placed inside a dedicated folder with a corresponding case ID. Each dataset contains a metadata file and one or several nomenclature files providing information about the content of the dataset and the characteristics of the case building cluster, and how this data has been generated. The datasets contain time series of buildingand energy-related monitoring variables (e.g., total power demand, grid incentive signal, outdoor temperature, etc) for reference (ref) scenarios (no demand response activations) and flexible (flex) scenarios (demand response activations). These datasets are analyzed in a dedicated scientific publication. In the dataset folder, one can find the Python file "dataset_treatment_and_analysis.py". This script runs a validity verification of the entire collection of datasets and, if all datasets are valid, generates a series of plots with the data from the different datasets. This Python script can be used as a basis to develop further data treatment and analysis processes. Detailed information The dataset template and metadata template of this collection of datasets are very detailed and rich. It is rare that all variables and all metadata information can be filled in for each case. If there is no data or information for a given variable or metadata feature, the column/cell is left empty. For each dataset, there is a corresponding metadata file with general information about the dataset case (e.g., “Metadata_case_A1_S1.xlsx”). In addition, there are variable nomenclature files (e.g., “Variable_nomenclature_aggregated_data_cluster_level_case_A1_S1.xlsx”) describing the different variables in the data files in details and providing information about the time aggregation/computation of each variable. IMPORTANT: The data csv files are in standard csv format with a comma (,) as a column separator and a point (.) as a decimal separator. All information, data, and values are represented in S.I. units or derived S.I. units (W, J, m, m2, °C, ppm, etc). Regarding the grid incentive signal variable (“grid_incentive_signal”): The grid incentive signal is often (but not necessary) an energy dynamic price signal. In the case of an energy price signal, one should set/define the unit of this signal with the local currency of where the building cluster is located (e.g., local currency per amount of energy or power use). It is also possible that the grid incentive signal is not a price signal; for instance, it can be a grid CO2 intensity signal or a grid congestion signal. The grid incentive signal can also be nondimensional (without unit). The description and unit of the grid incentive signal are indicated in the metadata file (“Grid incentive signal type”). The unit of the grid incentive signal is also stated in the “Variable_nomenclature_xxx” files. The ”demand_response_activation” variable is usually representative of the average activation of the flexible assets/targets in the entire building cluster (detailed information can be found in the variable nomenclature file). This variable is related to the grid incentive signal but is computed by different control strategies. Missing data points are represented by an empty cell/string, which should be considered as a “Not a Number” (NaN) or “not Available” (NA) when performing data treatment and analysis. Certain data files (especially metadata files) contain entries that are in the form of a list of strings or a list of numeric values. The different items in such lists are separated by a semicolon (;). Each dataset file contains variables related to the flexible operation of the building systems (activation of demand response events) and, when available, corresponding variables related to the non-flexible (contra-factual reference) operation of the building systems (no activation of the demand response events). The variables related to flexible operation are denominated with a name ending with “_flex”. The variables related to non-flexible/reference operation are denominated with a name ending with “_ref”. Variables that do not end with “_flex” or “_ref” are not directly related to the building operation or are invariant to the type of building operation (insensitive to whether or not the building performs demand response). For the spatial aggregation of extensive variables (e.g., energy, power, surface area) over several zones or buildings, the quantities should simply be summed. E.g., the variable “total_aggregated_energy_use_ref” is energy demand (over the last time step) of all monitored systems summed over the entire building or cluster of buildings. The variable “flexible_targets_aggregated_average_power_use_ref” is the average (over the last time step) power demand of only target flexible systems summed over the entire building or cluster of buildings in the reference scenario. For the spatial aggregation of intensive variables (e.g., operative temperature, CO2 concentration, relative humidity) over several zones or buildings, weighted average values should be calculated using conditioned floor area as the weight factor (unless specified otherwise in the metadata file at “Spatial aggregation of intensive variable”). One should keep in mind that these datasets are about building clusters, which means that these buildings are fairly close geographically to each other. The weather data is thus assumed to be originating from the nearest weather station. Additional information on the weather data can be found in the metadata files at “Outdoor weather data information”. The recorded date and time stamp represents the date and time of the end of a measurement/evaluation/recording period whose variables’ records are related (see Figure 1). The date and time stamp notation follows the standard ISO 8601 with the indication of the time offset from the UTC: e.g., 2024-07-30T21:10:00+01:00. The time step size variable of the current measurement/evaluation/recording period corresponds to the time interval (in seconds) between the current date and time stamp and the previous one (if it exists). If the previous time stamp does not exist (typically the case for the first data line of a data file), the time step size variable might not be defined (NaN) for this data line, but can be assumed from the next data line. Figure 1: Definition of the time stamp, time step size, and measurement evaluation recording period. Each data line has a date and time stamp (“date_time”). This “date_time” variable corresponds to the current date and time stamp at which the data variables are recorded/assigned in the dataset. However, the recorded values of these variables do not necessarily correspond to the actual value in the system at the current time stamp of recording, but is usually aggregated or integrated over the length of the previous time step ending at the current time stamp (see figure above). This “temporal aggregation/computation method” might differ from one variable to another and from one dataset to another. The naming of the variable can indicate a certain type of “temporal aggregation/computation method”, e.g., the variable “total_aggregated_max_power_use_ref” corresponds to the maximum value of the total aggregated power use over the previous time step. For the variable “total_aggregated_average_power_use_flex”, the recording of the power use corresponds to the average power over the previous time step. In addition, the metadata file contains information about the “temporal aggregation/computation method” method for the different recorded variables. Most of the variables, such as temperature, CO2 concentration, relative humidity, or average power, are usually recorded as an average of the measurand over the previous time step and assigned to the time stamp at the end of the time step. In this case, the metadata information “Time aggregation/computation method over the time step/measurement period: xxx” would simply state “Average”. Similarly, the variables that the name contains “max” or “min” are usually computed as the simple maximum or minimum of the measurand over the previous time step, such as “flexible_targets_aggregated_max_power_use_ref” or “flexible_targets_aggregated_min_power_use_ref”. However, other “temporal aggregation/computation method” methods are possible to compute the “average”, “min” and “max” variables: e.g., average rounded up (average value over the time measurement period, then rounded up); average rounded down (average value over the time measurement period, then rounded down); median (median value over the time measurement period); first (first value at the beginning of the time measurement period); last (last value at the end of the time measurement period); half-time (value recorded in the middle of the measurement period), etc. Once again, information about the “temporal aggregation/computation method” or computation of the different variables can be found in the metadata file at “Time aggregation/computation method over the time step/measurement period: xxx”. In the case of the energy-related variables (e.g., “flexible_targets_aggregated_energy_use_ref”), they are computed as the energy demand over the previous time step. By default, it is assumed that all variables related to energy (power) demand/supply are expressed as final energy (power) demand/supply. If this is not the case (e.g., primary energy demand/supply), it is explicitly stated in the metadata file at “Definition energy and power”. In each dataset, some of the variables are redundant: one can be calculated from another one and vice-versa. For example, the “flexible_targets_aggregated_energy_use_ref” variable can be calculated from the “flexible_targets_aggregated_average_power_use_ref” variable and the “time_step_size” variable. Conversely, the “flexible_targets_aggregated_average_power_use_ref” variable can be calculated from the “flexible_targets_aggregated_energy_use_ref” variable and the “time_step_size” variable. These redundant variables are set to ease the work of contributors when creating a new dataset: they have a choice in what key variable to include with minimum data transformation efforts. When creating a new dataset, one can omit redundant variables. If one decides to integrate redundant variables into a dataset, one has to make sure that the redundant variables are coherent with each other: e.g., if including both “flexible_targets_aggregated_average_power_use_ref” and “flexible_targets_aggregated_energy_use_ref” variables, one has to make sure that “flexible_targets_aggregated_average_power_use_ref” [W] x “time_step_size” [s] = “flexible_targets_aggregated_energy_use_ref” [J]. Dataset naming conventions The different datasets are denominated as in the following example: “case_A1_S1”. The case letter “A” corresponds to the group or initiative from which the dataset originates. For instance, “A” identifies here the datasets originating from a single researcher who participated in the common simulation exercise of the IEA EBC Annex 82. The case number “1” corresponds to a specific building or cluster of buildings in a given context and boundary conditions. If a case has a different geometry, or properties (e.g., insulation level, location, control system), or weather conditions, or evaluation period, or control algorithm, or occupancy schedule, its case number will be different. Any change in the case, except for the grid incentive signal, will lead to a new case number. The scenario code “S1” corresponds to the scenario number from which the dataset originates. Scenario variations only concern changes in the incentive grid signal received by the buildings, e.g., a different dynamic price signal. For the disaggregated data at the building level, the data file naming convention should follow the example hereafter: Disaggregated_data_building_level_case_A1_S1_building_0001.csv For the disaggregated data at the zone level, the data file naming convention should follow the example hereafter: Disaggregated_data_building_thermal_zone_level_case_A1_S1_building_0001_Zone_001.csv Structure of the collection of datasets on demand response from buildings and clusters of buildings Collection_of_datasets ├─ README.txt ├─ dataset_treatment_and_analysis.py ├─ Metadata_collection_of_datasets ├─ Description_structure_notes_collection_of_datasets.docx (current file) ├─ Header_dataset_cluster_level.csv ├─ Variable_nomenclature_aggregated_data_cluster_level_template.xlsx ├─ Variable_nomenclature_disaggregated_data_building_level_template.xlsx ├─ Variable_nomenclature_disaggregated_data_building_thermal_zone_level_template.xlsx ├─ Metadata_case_template.xlsx ├─ Dataset_mapping.xlsx └─ Metadata_collection_of_datasets.xlsx ├─ Datasets_case_A1_S1 ├─ Metadata ├─ Metadata_case_A1_S1.xlsx ├─ Variable_nomenclature_aggregated_data_cluster_level_case_A1_S1.xlsx ├─ Variable_nomenclature_disaggregated_data_building_level_case_A1_S1.xlsx (optional) ├─ Variable_nomenclature_disaggregated_data_building_thermal_zone_level_case_A1_S1.xlsx (optional) ├─ building_model_case_A1_S1 (optional) └─ Data_model_case_A1_S1.xxx (optional) ├─ Aggregated_data_cluster_level └─ Aggregated_data_cluster_level_case_A1_S1.csv ├─ Disaggregated_data_building_level (optional) ├─ Disaggregated_data_building_level_case_A1_S1_building_0001.csv ├─ Disaggregated_data_building_level_case_A1_S1_building_0002.csv … └─ Disaggregated_data_building_level_case_A1_S1_building_0098.csv ├─ Disaggregated_data_building_thermal_zone_level (optional) ├─ Disaggregated_data_building_thermal_zone_level_case_A1_S1_building_0001_Zone_001.csv ├─ Disaggregated_data_building_thermal_zone_level_case_A1_S1_building_0001_Zone_002.csv … └─ Disaggregated_data_building_thermal_zone_level_case_A1_S1_building_0001_Zone_012.csv ├─ Additional_case_information (optional) └─ Raw_data (optional) ├─ Datasets_case_A1_S2 ├─ Datasets_case_A2_S1 … └─ Datasets_case_F4_S9