A multi-step approach to evaluating the effect of temperature and humidity on accuracy of solar energy prediction using hybrid DTCN-LSTM

Abstract

Accurate solar energy prediction is critical for optimising renewable energy systems, particularly in regions with diverse climatic conditions. This study investigates the impact of humidity and temperature on solar energy prediction accuracy in Ghanaian climates. It uses a hybrid Dilated Temporal Convolutional Network and Long Short-Term Memory (DTCN-LSTM) model to capture diurnal patterns and normalised temperature, humidity, and solar irradiance data from 2010–2022. The results show exceptional predictive accuracy, with R² values exceeding 99.86 and near-zero RMSE values of less than 0.0055 kWh across all areas studied, representing 0.098 percent of the designed 560Wh of the solar energy simulated. Temperature exclusion caused the largest performance decline, while humidity exclusion had negligible beneficial effects. The DTCN-LSTM model exhibited strong generalisation, with minimal training-testing discrepancies R² ≤ 0.07%, and maintained high R² accuracy of greater than 99.87% even when both temperature and humidity were excluded, highlighting its adaptability to sparse sensor environments. The findings show that prioritising temperature data collection over humidity improves solar energy prediction accuracy, and the DTCN-LSTM robustness across diverse climates is a vital tool for enhancing grid stability and energy storage planning in variable environments. This work further advances adaptive machine learning frameworks for renewable energy systems, emphasising scalability and operational practicality in global solar forecasting applications.