A sequential ensemble model for photovoltaic power forecasting. (December 2021) - Sharma, Nonita; Mangla, Monika; Yadav, Sourabh; Goyal, Nitin; Singh, Aman; Verma, Sahil; Saber, Takfarinas
Highlights: A new hybrid deep learning based framework for photovoltaic power forecasting is proposed. The framework integrates long short term memory layer with vanishing time series gradient and maximal overlap discrete wavelet transform series. MODWT is implemented using a multiresolution pyramidal hierarchical decomposition technique. The proposed method outperforms previous models and establishes its efficacy even for longer intervals. Abstract: During this era of the energy crisis, when the non-renewable sources are rapidly diminishing, efforts are being taken to utilize renewable sources predominantly. This manuscript presents a hybrid deep learning framework using long short term memory (LSTM) Layer with vanishing time series gradient and maximal overlap discrete wavelet transform (MODWT) model for photovoltaic (PV) power forecasting through time series decomposition. The proposed framework is implemented on the dataset collected from Yulara Solar System, Australia. During the experimental evaluation, obtained results demonstrate short term temporal dependence of PV power forecasting on solar power magnitudes as well as weather conditions. Moreover, the proposed model outperforms existing state-of-the-art models in terms of mean average percentage error (MAPE) by 14.17%, 3.01%, and 16.49% for 1 day, 10 days, and 1 month, respectively, establishing its efficacy even for longer intervals. Graphical abstract: Proposed Ensemble Model divided into three stages viz. Time series decomposition and reconstruction, forecasting phase, and weighted aggregation of predicted results Image, graphical abstract