Hybrid short-term forecasting of the electric demand of supply fans using machine learning

This paper presents the development and application of multi-step-ahead short-term forecasting models targeting supply fans installed in an institutional building. The models applied in this work consist of an artificial neural network (ANN) applied in order to forecast the future supply air flow rate of the fans (black-box approach), and a physical model coupled with the ANN applied in order to forecast the future electric demand of the supply fans (hybrid grey-box approach). The forecasting models use measurement data obtained at 15-min intervals in order to forecast the target variables over the next 6 h. The architecture of the ANN was found through an automated search in the training data set. The paper compares the results of selected ANN models with those from other machine learning techniques (support vector regression and ensemble methods) along with a simple forecasting approach. The results of this study show a better forecasting performance when compared with the results from other publications: the CV(RMSE) is 1.8-3.4% for the air flow rate, and 4.8-7.3% for the electric demand for all new models. The results demonstrate that automating the hyperparameter search of the ANN architecture can help alleviate the difficulty of manual parameter setting and achieve a high performing model.