Multi-Zone Office HVAC Dataset with Real External Signals and Multi-Horizon Price Forecasts: A Four-Year Benchmark
Description
This dataset was developed for research on forecast-assisted HVAC control, building energy management, demand response, thermal comfort, and indoor air quality in multi-zone office buildings. The modeled building consists of eleven heterogeneous zones, including eight office rooms, one hall/corridor, one meeting room, and one conference room, all served by a centralized thermal-conditioning and ventilation system. The dataset covers four consecutive years, from January 1, 2020, to December 31, 2023, at a 30-minute temporal resolution. It combines real external signals, including CAISO real-time electricity prices and NSRDB weather and solar-resource variables, with simulated office occupancy, meeting and conference events, and control-oriented metadata describing the thermal and ventilation characteristics of each zone. Six-step electricity-price forecasts, covering the next 30 minutes to three hours, are provided for every timestamp. These forecasts were generated using a Peak-Aware Multi-Horizon Dual-Attention LSTM model (PA-MHDA-LSTM), which combines an LSTM encoder–decoder architecture with encoder self-attention, temporal context aggregation, future-time embeddings, horizon embeddings, and decoder-to-history cross-attention. The forecasting pipeline follows a strictly chronological and leakage-free evaluation procedure. Data normalization and model fitting use only the training period, model selection is performed on the evaluation period, and the test-period forecasts are generated without access to future electricity-price targets. Only historical observations and known future calendar features are used to predict each forecasting horizon. The data are divided chronologically into training data for 2020–2021, evaluation data for 2022, and test data for 2023. Each subset contains the office operating data, six-horizon electricity-price forecasts, and zone metadata. The dataset is intended for reproducible evaluation of reinforcement-learning, model-predictive, rule-based, forecasting-assisted, and other intelligent HVAC control methods.
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Institutions
- Ferdowsi University of MashhadRazavi Khorasan, Mashhad