A deep attention-driven model to forecast solar irradiance

Abstract

Accurately forecasting solar irradiance is indispensable in optimally managing and designing photovoltaic systems. It enables the efficient integration of photovoltaic systems in the smart grid. This paper introduces an innovative deep attentiondriven model for solar irradiance forecasting. Notably, an extended version of the variational autoencoder (VAE) is introduced by amalgamating the desirable characteristics of the bidirectional LSTM (BiLSTM) and attention mechanism with the VAE model. Specifically, the introduced approach enables the conventional VAE's ability to model temporal dependencies by incorporating BiLSTM at the VAE's encoder side to better extract and learn temporal dependencies embed on the solar irradiance concentration measurements. In addition, the self-attention mechanism is embedded in the VAE's encoder side following the BiLSTM to highlight pertinent features. The performance of the proposed model is evaluated through comparisons with the recurrent neural network (RNN), gated recurrent unit (GRU), LSTM, and BiLSTM. Measurements of solar irradiance in the US and Turkey are used to evaluate the investigated models. Results confirm the superior performance of the proposed model for solar irradiance forecasting over the other models (i.e., RNN, GRU, LSTM, and BiLSTM).

Predicting Solar Irradiance at Several Time Horizons Using Machine Learning Algorithms

“…Recently, there has been a greater emphasis on research relating to estimating solar irradiance due to the ever-increasing demand for, and interest in, green energy [6]. It is crucial to accurately forecast solar irradiance when designing and managing photovoltaic (PV) systems [7,8]. For the power grid to run smoothly, or for the best control of the energy flows into the solar system, it is necessary to forecast the output power of solar systems.…”