Accurate short-term GHI forecasting using a novel temporal convolutional network model

Abstract

Global energy demand is on the rise, driven by factors such as population growth and economic development.Utilizing renewable energy is crucial to meeting this energy demand in light of global warming and the depletion of fossil resources.One of the renewable energy sources that is extensively utilized in multiple countries worldwide is photovoltaic energy.While integrating photovoltaic (PV) energy into the grid offers substantial economic and environmental advantages, its intermittent nature presents challenges to maintaining grid stability at high penetration levels. Accurate solar energy estimations are essential for photovoltaic (PV) based energy facilities to enable early participation in energy markets and efficient resource planning. This study proposes and evaluates a Temporal Convolutional Network (TCN) model that can rapidly predict global horizontal irradiance (GHI) using univariate and multivariate approaches. To our knowledge, this is the first study to employ a TCN model for GHI forecasting. The univariate model solely considers GHI, while the multivariate time series models incorporate a combination of six variables: global horizontal irradiance, active power of a module plant, PV module temperature, wind speed, air temperature, and air pressure. The proposed model’s effectiveness is validated by comparing its performance to benchmark models, including MLP, RNN, GRU, and LSTM. The prediction models’ performance is evaluated using root mean square error (RMSE), coefficient of determination (R2), and mean absolute error (MAE). The results demonstrate that the effectiveness of univariate TCN models is evident in their performance for 5, 10, 15, and 20 min ahead short-term GHI forecasting, its mean absolute error (MAE) values achieved are 22.935 W/m2 33.47 W/m2, 36.9994 W/m2, and 41.9946 W/m2, respectively. The multivariate model, incorporating additional variables, yielded higher MAE values : 37.928 W/m2, 55.88 W/m2, 66.61 W/m2, and 74.82 W/m2, respectively. These results suggest that the TCN model’s capability to capture both long-term and short-term dependencies within the data contributes to its accuracy for GHI forecasting compared to other models.