Deep learning for renewable energy forecasting: A review

Improving the accuracy of renewable energy forecasting is important to power system planning, management, and operations as renewable energy becomes more prevalent in the worldwide electric energy grid. Due of the sporadic and unpredictable nature of renewable energy data, this is a difficult job. To date, a variety of approaches have been developed to enhance the forecasting accuracy of renewable energy, including physical models, statistical methods, artificial intelligence techniques, and their hybrids. Deep learning has been widely described in the literature as a potential form of machine learning capable of finding intrinsic nonlinear characteristics and high-level invariant structures in data. This article offers a thorough and in-depth examination of deep learning-based renewable energy forecasting techniques in order to assess their efficacy, efficiency, and application potential. Deep belief network, stack auto-encoder, deep recurrent neural network, and others are the four categories of extant deterministic and probabilistic forecasting techniques based on deep learning. To enhance forecasting accuracy, we also analyze viable data pre-processing approaches and error post-correction procedures. Various deep learning-based forecasting techniques are thoroughly examined and discussed. Finally, we look at present research efforts, problems, and future research goals in this area.