Neural networks based multivariate time series forecasting of solar radiation using meteorological data of different cities of Bangladesh

Abstract

Solar radiation is the energy or radiation we get from the sun, time-varying data. Solar radiation plays a vital role in various sectors. With better prediction, performances in these sectors can be enhanced. In this work, we proposed a system to forecast solar radiation using Neural Networks. Meteorological data from five different cities of Bangladesh were used. The system can forecast radiation values for any day using different meteorological data from the previous day. Three different networks were trained using the meteorological data, which are the Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). Also, predictions were made for all five cities separately. An elaborate evaluation of all three models has been done to produce a comparison using widely used performance metrics. The GRU model produced the best result among all three models, with a MAPE score of 19.28%.

Keywords

• Recurrent neural network
• Long short-term memory
• Gated recurrent unit
• Solar radiation
• Neural network
• Time series forecasting