Deep q-learning framework based solar energy generation prediction

Abstract

In the recent years, the power system market has seen a huge shift towards the utilisation of Renewable energy sources (RES) as candidates of power generation since they proved to be a great alternative of conventional sources due to its low carbon footprints and less dependency over fossil fuels, thereby increasing the penetration of RES in microgrids. RES sources like Wind, Tidal, Hydro, Solar etc. are widely available today, among which Solar is the most popular source of energy due to its cheap running cost and easy installation. However, Solar faces complications such as intermittency which is a very big drawback to its applicability and reliability thus requiring additional strategies to increase its resiliency. A short term forecasting of solar generation might be a great solution to observe the intermittency and predict the future generation based on various factors. In this research work a Deep Q-learning framework was proposed to predict the Solar Generation and provide predictive results for special months and days like Spring equinox, Summer Solstice, Autumn Equinox and Winter Solstice. The Deep Q-learning (DQNN) framework is an amalgamation of Deep Learning networks and Q-learning technique that exploits the properties of Deep Learning networks and Q-learning technique to map the state-Q-value pair and perform the prediction process. The simulations of DQN network was performed in an open-source platform known as Keras and the prediction results were compared both in simulated and experimental datasets with other well known Deep learning networks such as CNN, LSTM, GRU and CNN-LSTM.