EMD-PSO-ANFIS-based hybrid approach for short-term load forecasting in microgrids

Accurate renewable energy generation and electricity demand forecasting tools constitute an essential part of the energy management system functions in microgrids. This study proposes a hybrid approach for short-term load forecasting in microgrids, which integrates empirical mode decomposition (EMD), particle swarm optimisation (PSO) and adaptive network-based fuzzy inference systems (ANFISs). The proposed technique first employs EMD to decompose the complicated load data series into a set of several intrinsic mode functions (IMFs) and a residue, and PSO algorithm is then used to optimise an ANFIS model for each IMF component and the residue. The final short-term electric load forecast value could be obtained by summing up the prediction results from each component model. The performance of the proposed model is examined using load demand dataset of a case study microgrid in Beijing and is compared with four other forecasting methods using the same dataset. The results show that the proposed approach yielded superior performance for short-term forecasting of microgrid load demand compared with the other methods.