Deep learning and statistical methods for short- and long-term solar irradiance forecasting for Islamabad

Abstract

The growing threat of global climate change stemming from the huge carbon footprint left behind by fossil fuels has prompted interest in exploring and utilizing renewable energy resources. Several statistical, Machine and Deep Learning techniques exist and have been used for many years for a range of forecasting problems. This study is based on the data recorded for 4 years and 9 months using precise instruments, in Islamabad, Pakistan. For this purpose we use statistical and Deep Learning architectures for forecasting solar Global Horizontal Irradiance which not only helps in grid management and power distribution, but also brings attention towards the potential of solar power production in Pakistan and its part to play in tackling global climate change. We have used statistical methods such as Seasonal Auto-Regressive Integrated Moving Average Exogenous (SARIMAX) and Prophet, and Machine Learning methods such as Long Short-Term Memory (LSTM) which is an extension of Recurrent Neural Networks (RNN), Convolutional Neural Network (CNN) and Artificial Neural Network (ANN). The selected forecast methods in our study are based on their ability to work with time series data and we have used different models configurations to see which performs best for our dataset. The performance of every model is studied using different error metrics such as Coefficient of Determination (R2), Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE). The major contribution of this study is the data collected to carry out research towards the goal of renewable energy future, and from the test methods used on the data in this study, it can be intuitively determined that ANN, CNN, and LSTM architectures perform best for short-term forecasts, while SARIMAX and Prophet are efficient for long-term forecasts.