Short-Term PV Power Forecasting Based on CEEMDAN and Ensemble DeepTCN

With the high percentage access of photovoltaic (PV) power generation, accurate and stable short-term PV power generation forecasting has become popular to the existing power system planning and operation. This paper proposes an ensemble learning method based on signal decomposition, deep learning, and optimization strategy for forecasting short-term PV power. At first, the original PV series is decomposed by utilizing the complete ensemble empirical mode decomposition with adaptive noises (CEEMDAN). Then, the decomposed PV series are separately allocated to different deep temporal convolutional networks (DeepTCNs) for forecasting. Finally, the multi-verse optimizer strategy based on no-negative constraint theory (NNCT) is introduced to integrate the weight coefficients of the ensemble DeepTCNs strategy and reconstruct eventual forecasting results. The case studies on real-time PV data from Alice Springs, Australia, present that the proposed method is superior to other benchmark methods in four conventional performance indexes and two statistical tests, demonstrating the validity of the proposed method in forecasting PV power.