本数据集为2016年1月1日至2016年12月30日德国居民家庭负荷与光伏发电时间序列数据集，旨在为家庭能源管理、负荷预测、需求响应、分布式能源优化及低压配电网建模等研究提供标准化的用户侧能源数据支撑。数据集基于Open Power System Data（OPSD）开放电力系统数据平台发布的Household Data数据包构建，原始数据来源于德国南部居民家庭的实际测量数据，通过智能电能计量设备持续记录用户用电负荷、分布式光伏发电以及部分设备级用电信息。
\n本数据集包含6个CSV表格文件，分别为resampled_residential_0.csv、resampled_residential_1.csv、resampled_residential_2.csv、resampled_residential_3.csv、resampled_residential_4.csv和resampled_residential_5.csv。其中，
\n• resampled_residential_0.csv为住宅0居民家庭负荷与光伏发电时序数据，包含8个数据要素，分别为：time、dishwasher、freezer、grid_import、heat_pump、pv、washing_machine和grid_export；
\n• resampled_residential_1.csv为住宅1居民家庭负荷时序数据，包含7个数据要素，分别为：time、circulation_pump、dishwasher、freezer、grid_import、washing_machine和grid_export；
\n• resampled_residential_2.csv为住宅2居民家庭负荷与光伏发电时序数据，包含9个数据要素，分别为：time、circulation_pump、dishwasher、freezer、grid_import、pv、refrigerator、washing_machine和grid_export；
\n• resampled_residential_3.csv为住宅3居民家庭负荷与光伏发电时序数据，包含10个数据要素，分别为：time、dishwasher、ev、freezer、grid_import、heat_pump、pv、refrigerator、washing_machine和grid_export；
\n• resampled_residential_4.csv为住宅4居民家庭负荷时序数据，包含6个数据要素，分别为：time、dishwasher、grid_import、refrigerator、washing_machine和grid_export；
\n• resampled_residential_5.csv为住宅5居民家庭负荷与光伏发电时序数据，包含8个数据要素，分别为：time、circulation_pump、dishwasher、freezer、grid_import、pv、washing_machine和grid_export。
\n此外，time为时间戳字段，用于标识数据采集时刻，数据采样时间间隔为1小时；dishwasher表示住宅建筑内洗碗机能耗；freezer表示住宅建筑内冷冻设备能耗；refrigerator表示住宅建筑内冰箱能耗；washing_machine表示住宅建筑内洗衣机能耗；circulation_pump表示住宅建筑循环泵能耗；heat_pump表示住宅建筑热泵能耗；ev表示住宅建筑电动汽车充电负荷；pv表示住宅建筑光伏发电量；grid_import表示住宅建筑从公共电网购入电能；grid_export表示住宅建筑向公共电网输出电能。除时间字段外，其余能源数据均采用千瓦时（kWh）作为计量单位，可反映居民家庭典型设备负荷、分布式光伏发电以及与公共电网之间的能量交互过程。
\n数据集同时涵盖设备级负荷、分布式能源发电及电网能量交换信息，保留真实家庭运行场景下的时序相关性、周期性与随机波动特征，能够较为全面地刻画居民家庭能源系统的运行规律。该数据集可广泛应用于智能电网、能源互联网、综合能源系统、多智能体协同优化、强化学习能源调度、需求响应分析以及用户侧能源管理等领域，为相关算法开发、模型训练、方法验证与性能评估提供可靠的数据基础与实验支撑。
", "ds_source": "本数据集来源于Open Power System Data（OPSD）开放电力系统数据平台公开发布的Household Data数据包，通过公开网站下载获取。Open Power System Data是面向电力系统研究社区建设的开放数据服务平台，专注于收集、整理、校验、处理并发布来源于公开渠道但难以直接使用的电力系统数据资源。本研究直接从Open Power System Data官方网站下载并使用该公开数据集，虽然未对原始数据采集过程进行任何修改，但为满足后续分析和建模的要求，对下载的数据进行了进一步处理和整理。
", "ds_process_way": "本数据集基于Open Power System Data（OPSD）平台发布的Household Data数据包进行加工处理，数据处理过程主要依托Python语言及Pandas、NumPy等开源数据分析工具完成。首先，对原始数据进行读取与时间格式标准化处理。通过正则表达式提取有效时间戳信息，并将UTC格式时间统一转换为标准时间序列格式，同时移除时区信息，构建统一的时间索引。随后删除与研究任务无关的辅助字段，包括夏令时标识字段和数据插值标识字段等。其次，对数据质量进行清洗。将原始数据中的缺失值标识符统一替换为标准缺失值（NaN），并依次删除全为空值的特征列、除时间字段外全为空值的样本记录、全零特征列以及仅包含单一取值的无效特征列，以消除冗余信息对后续分析的影响。随后，对各居民用户数据进行独立处理。通过去除数据列名前缀，统一不同用户的数据字段命名格式；根据研究需求筛选指定时间范围内的数据样本；针对关键负荷、光伏发电及电网交互变量，删除目标变量缺失的异常记录，确保保留样本具有完整有效的信息。最后，对处理后的家庭用户负荷、光伏发电及设备级用电数据进行统一整理与存储，形成适用于负荷预测、需求响应、能源管理及强化学习调度研究的标准化时间序列数据集。
", "ds_quality": "本数据集在获取、清洗与汇集过程中实施了系统性质量控制，确保了数据的完整性、一致性和可用性。经核查，原始数据集共包含38454条时间记录和71个数据字段，时间范围覆盖2014年12月11日至2019年5月1日，数据时间分辨率为1小时。经过缺失值检查、无效特征筛除及格式标准化处理后，最终保留38454条有效记录和69个数据字段，删除了2个无效特征字段。数据集中共包含工业用户（industrial1–industrial3）、公共用户（public1–public2）以及居民用户（residential1–residential6）共11类用户数据。其中，居民用户数据被进一步独立整理为6个家庭用户数据文件，分别对应residential1至residential6。所有数据文件均成功生成，字段命名规范统一，时间索引格式一致，不存在重复时间记录。针对关键变量进行了完整性检查，保留的居民用户数据均包含电网购电（grid_import）、分布式光伏发电（pv）、设备级负荷（如dishwasher、freezer、washing_machine、heat_pump等）中的有效观测信息。对于存在缺失值的异常样本已进行筛除，最终保留数据不存在关键目标变量缺失问题。以residential1用户为例，经时间范围筛选后共保留2016年全年8760个小时样本，时间范围为2016年1月1日00:00至2016年12月30日23:00，各变量缺失值数量均为0，数据记录连续完整。处理后的数据集能够真实反映居民家庭负荷变化、分布式光伏发电及设备运行特征，可为负荷预测、需求响应、能源管理及强化学习调度等研究提供可靠的数据支撑。
", "ds_acq_start_time": "2016-01-01 00:00:00", "ds_acq_end_time": null, "ds_acq_place": "德国", "ds_acq_lon_east": null, "ds_acq_lat_south": null, "ds_acq_lon_west": null, "ds_acq_lat_north": null, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 4419100, "ds_files_count": 0, "ds_format": "csv", "ds_space_res": "", "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "9824800d-e7e1-4252-a56b-a18c61483dc9.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": "", "organization_id": "9ecaaa78-39e9-411e-9f24-274e12aa643f", "ds_serv_man": "虞文武", "ds_serv_phone": "15051861330", "ds_serv_mail": "wwyu@seu.edu.cn", "doi_value": "", "subject_codes": [ "410" ], "quality_level": 0, "publish_time": "2026-07-09 10:58:42", "last_updated": "2026-07-09 10:58:42", "protected": false, "protected_to": "2028-06-30 00:00:00", "lang": "zh", "cstr": "", "i18n": { "en": { "title": "Residential Load and Photovoltaic Generation Time-Series Dataset", "ds_format": "csv", "ds_source": "The dataset used in this study originates from the Household Data package provided by the Open Power System Data (OPSD) platform and was acquired through public download from the official OPSD website. Open Power System Data is an open-access data platform dedicated to electricity system research, providing curated, validated, processed, and documented datasets collected from publicly available sources.\r\n\r\nThis study directly downloaded and utilized the publicly available dataset from the official OPSD website. No modifications were made to the original data collection process; however, the downloaded data were further processed and curated to meet the requirements of subsequent analysis and modeling.", "ds_quality": "Systematic quality-control procedures were applied during data acquisition, cleaning, and integration to ensure the completeness, consistency, and usability of the dataset. The original dataset contained 38,454 time-series records and 71 data fields, covering the period from December 11, 2014, to May 1, 2019, with an hourly temporal resolution. After missing-value inspection, invalid-feature removal, and data standardization, the final dataset retained 38,454 valid records and 69 data fields, with two non-informative features removed.\r\n\r\nThe dataset includes 11 categories of users, consisting of three industrial users (industrial1–industrial3), two public users (public1–public2), and six residential users (residential1–residential6). The residential-user data were further organized into six independent household datasets corresponding to residential1 through residential6. All datasets were successfully generated, with standardized variable naming conventions, consistent timestamp formats, and no duplicate temporal records.\r\n\r\nCompleteness checks were performed on key variables. The retained residential datasets contain valid observations for electricity import from the grid (grid_import), photovoltaic generation (pv), and appliance-level loads such as dishwashers, freezers, washing machines, and heat pumps. Records with missing values in critical target variables were excluded, ensuring that the final datasets contain no missing values in key variables.\r\n\r\nFor example, the residential1 dataset contains 8,760 hourly samples covering the entire year of 2016, from 00:00 on January 1, 2016, to 23:00 on December 30, 2016. Quality inspection confirmed that all variables contain zero missing values and that the time series is continuous and complete. The processed dataset preserves realistic household load dynamics, distributed photovoltaic generation characteristics, and appliance-level operating behaviors, providing reliable support for research on load forecasting, demand response, energy management, and reinforcement learning-based energy scheduling.", "ds_ref_way": "", "ds_abstract": "This dataset contains time-series data of residential electricity consumption and photovoltaic (PV) generation from German households covering the period from January 1, 2016 to December 30, 2016. It is designed to provide standardized user-side energy data support for research on home energy management, load forecasting, demand response, distributed energy optimization, and low-voltage distribution network modeling. The dataset is constructed based on the Household Data package released by the Open Power System Data (OPSD) platform. The original data were collected from real residential households in southern Germany using smart metering devices that continuously recorded electricity consumption, distributed PV generation, and appliance-level energy usage information.\r\n\r\nThe dataset consists of six CSV files, namely resampled_residential_0.csv, resampled_residential_1.csv, resampled_residential_2.csv, resampled_residential_3.csv, resampled_residential_4.csv, and resampled_residential_5.csv. Specifically, \r\n• resampled_residential_0.csv contains time-series data for Residential Household 0, including eight data elements: time, dishwasher, freezer, grid_import, heat_pump, pv, washing_machine, and grid_export.\r\n• resampled_residential_1.csv contains time-series data for Residential Household 1, including seven data elements: time, circulation_pump, dishwasher, freezer, grid_import, washing_machine, and grid_export.\r\n• resampled_residential_2.csv contains time-series data for Residential Household 2, including nine data elements: time, circulation_pump, dishwasher, freezer, grid_import, pv, refrigerator, washing_machine, and grid_export.\r\n• resampled_residential_3.csv contains time-series data for Residential Household 3, including ten data elements: time, dishwasher, ev, freezer, grid_import, heat_pump, pv, refrigerator, washing_machine, and grid_export.\r\n• resampled_residential_4.csv contains time-series data for Residential Household 4, including six data elements: time, dishwasher, grid_import, refrigerator, washing_machine, and grid_export.\r\n• resampled_residential_5.csv contains time-series data for Residential Household 5, including eight data elements: time, circulation_pump, dishwasher, freezer, grid_import, pv, washing_machine, and grid_export.\r\nThe field time represents the timestamp used to identify the data acquisition moment, with a sampling interval of one hour. All energy-related variables are measured in kilowatt-hours (kWh). The variables dishwasher, freezer, refrigerator, washing_machine, circulation_pump, heat_pump, and ev represent appliance-level electricity consumption in residential buildings; pv represents photovoltaic power generation; and grid_import and grid_export represent electricity imported from and exported to the public grid, respectively.\r\n\r\nThe dataset captures appliance-level load profiles, distributed photovoltaic generation, and bidirectional grid energy exchange. It preserves the temporal correlations, periodic patterns, and stochastic variations observed in real residential operating environments, providing a comprehensive representation of household energy system behavior. This dataset can be widely applied to research areas such as smart grids, energy internet systems, integrated energy systems, multi-agent collaborative optimization, reinforcement learning-based energy scheduling, demand response analysis, and user-side energy management. It offers a reliable foundation for algorithm development, model training, method validation, and performance evaluation in energy-related applications.", "ds_time_res": "", "ds_acq_place": "Germany", "ds_space_res": "", "ds_projection": "", "ds_process_way": "The dataset was processed based on the Household Data package released by the Open Power System Data (OPSD) platform. All preprocessing procedures were implemented using Python and the open-source data analysis libraries Pandas and NumPy. First, the raw data were imported and standardized with respect to timestamp formatting. Valid UTC timestamps were extracted using regular expressions and converted into a unified datetime format, while timezone information was removed to ensure consistency across all records. Auxiliary fields not required for subsequent analysis, such as daylight-saving-time indicators and interpolation flags, were removed. Next, a data cleaning procedure was performed. Missing-value identifiers in the original files were replaced with standard NaN values. Columns containing only missing values, records with no valid observations except timestamps, all-zero columns, and columns with constant values were removed to eliminate redundant and non-informative features. Subsequently, each residential household dataset was processed individually. Variable-name prefixes associated with different households were removed to establish a consistent naming convention across datasets. Data samples were further filtered according to the selected study period. For key variables related to electricity consumption, photovoltaic generation, and grid interaction, records containing missing target values were excluded to ensure data completeness and reliability. Finally, the cleaned household load, photovoltaic generation, and appliance-level electricity consumption data were organized into a standardized time-series dataset suitable for load forecasting, demand response, energy management, and reinforcement learning-based energy scheduling studies.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "recommendation_value": 0, "license_type": "https://creativecommons.org/licenses/by/4.0/", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "belong_to_nieer": false, "ds_topic_tags": [ "家庭负荷", "光伏发电", "设备级能耗", "智能电表", "时间序列数据", "分布式能源" ], "ds_subject_tags": [ "工程与技术科学基础学科" ], "ds_class_tags": [], "ds_locus_tags": [], "ds_time_tags": [], "ds_contributors": [ { "true_name": "虞文武", "email": "wwyu@seu.edu.cn", "work_for": "东南大学数学学院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "虞文武", "email": "wwyu@seu.edu.cn", "work_for": "东南大学数学学院", "country": "中国" } ], "ds_managers": [ { "true_name": "虞文武", "email": "wwyu@seu.edu.cn", "work_for": "东南大学数学学院", "country": "中国" } ], "category": "其他" }