Hybrid AI improves Energy Forecasts by combining Fuzzy Rules, Evolutionary Strategies and Neural Networks

Currently, the need for more efficient use of energy is in the spotlight more than ever. For optimal energy management the forecast of energy consumption is of great interest.This paper takes a novel approach for forecasting the energy demand in households by using a hybrid AI approach. On the one hand, we use an interpretable model creation by using fuzzy rules. Those rules are then combined with an evolutionary strategy to create new simulation data which calibrates the reality and sometimes uncertainty behind the data. Based on this newly created data, a simple artificial neural network (ANN) model is created. It is shown, that there is no need to create an unnecessarily complex deep learning architecture for achieving good results. Simple ANN models can achieve excellent results, when using data created by inferred fuzzy rules. Of great advantage is, that one part of this hybrid AI approach can still be interpreted by humans and furthermore improved by adding the knowledge of human domain experts in form of fuzzy rules.