Forecasting Solar Power Output to Assist with the Integration of Solar Energy into the Power Grid

This paper discusses the integration of solar energy into the power grid by forecasting solar power ahead of time using a time series forecasting problem formulation. The aim is to help reduce the impact of fossil fuels on the environment. The two main objectives are namely to forecast PV power one timestep ahead and to forecast PV power multiple timesteps ahead. Data has been taken from the Hong Kong University of Science and Technology. This paper compares machine learning techniques such as CNN LSTM, RNN LSTM, Dense Neural Networks, and Convolutional Neural Networks, to find the best predictor of PV output. The tuning of hyperparameters such as the learning rate, regularization parameter, activation function, number of iterations, etc. is also an essential part of the discussion. The long short-term memory (LSTM) and dense feed-forward layers, as well as convolutional layers, help to introduce new important features from the original features. This paper gives a summary of the future works to be done to assist with additional research and improved results