Comparison of data-driven models for building energy load forecasting

Forecasting building energy loads is vital for smart energy management control systems that drive the energy efficiency of buildings. Data-driven forecasting models, learning from historical and real-time load data, offer advantages over traditional physics-based models, particularly in scenarios where detailed building information is not available.

Existing literature was reviewed for identifying the state-of-the-art models for the building energy load forecasting task. Although various methods have been applied, there was no clear consensus on which are the optimal models for each use case. The most popular methods included Artificial Neural Networks, and meteorological variables, such as outdoor temperature, were among the most frequently used features.

In this study, six data-driven models were implemented and compared for forecasting heating, cooling and electricity loads of an office building in Helsinki, Finland. Models included multi-variable linear regression (MLR), Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), Multi-layer Perceptron (MLP), Long Short-term Memory Network (LSTM) and Convolutional Neural Network (CNN). Pre-processing and feature selection were conducted for the data based on examples set by the existing studies.

Results demonstrated that among the implemented models, XGBoost excelled in heat load forecasting, while LSTM performed optimally for electricity load prediction. CNN and LSTM obtained the smallest errors for cooling load forecasting, but the data quality made it difficult to draw clear conclusions. In a case study, the best performing models for heating and cooling were implemented also to another building, for which the XGBoost and LSTM were found the best as well. However, interpreting evaluation metrics revealed inconsistencies between models. Models were also compared in terms of efficiency and required amount of training data. In general, deep learning models had longer training times. Variations in training data sets did not significantly impact model performance, although in most cases less data led to larger errors.

Limitations of the study included feature selection, which was conducted similarly for all the models. Future research should explore different feature sets and consider seasonal variations of heating and cooling loads for improved model accuracy.