Deep Learning Approach to Load Forecasting: A Survey

The power sector has been widely invested-in for many years. There is a need in finding lasting solutions that can ameliorate the ever-dynamic challenges attached to it which makes the researcher looking for techniques in artificial intelligence solving the complication in the power sector. Since when Artificial intelligence came to existence a lot of problems have been solved through the use of its application such as an artificial neural network (ANN), Neural Network (NN), Deep Neural Network (DNN), Machine learning (ML) and deep learning (DL). Deep learning has become a very good solving tool which makes research focus more on it to tackle a lot of problems such as forecasting tasks, modeling the non-linearity in data of many fields, computer vision, natural language processing, speech recognition, and signal processing. This updated review paper focuses on the application of deep learning (DL) that applied to solar load forecasting; the common algorithm used was shown in the literature reviews. The main reason for this review is to show the latest updated techniques using DL for forecasting that will help the researcher to select the best methods in DL for forecasting accurately. After the review it shows that deep learning performs better for forecasting showing good accuracy, finding the hidden layer.