Short-term building electrical load forecasting using adaptive neuro-fuzzy inference system (ANFIS)

The aim of this study is to develop very short-term and accurate energy consumption forecasts for educational building. The purpose is to develop a predictive model for building energy planning to balance the supply from renewable power systems and the building electrical load demand). For the methodology, an adaptive neuro-fuzzy inference system (ANFIS) was used as a machine learning approach for building energy forecasting. The data for training (80%), testing (10%), and validation (10%) was used from smart energy meters installed in the building and weather data. A total of 20520 dataset instances was used for developing the forecasting model. The originality of this study is the ANFIS model was validated for the training, testing, and validation data, and the accuracy and reliability of the forecasting model was assessed over various very short time horizon of past data (0.5-4 h ahead). The main results reveal that the predictive model is extremely accurate in predicting the energy consumption of a building. The values of coefficient of correlation R for training, testing, and validation are 0.98017, 0.9778, and 0.97593, respectively, for the 30 min ahead energy forecasting. The R values for all the data are respectively 0.97951, 09854, and 0,96778 for the 0.5, 1, 4 h ahead energy forecasting. The low nMSE and nMAE errors for the ANFIS model demonstrate how well the model replicates the reported results from smart energy meters. The research findings are very important for the energy planning of microgrid power systems (energy purchase, building operations and maintenance, and demand side management).