Renewable energy MicroGrid power forecasting: AI techniques with environmental perspective

Accurate power forecasting is a fundamental requirement for the reliable and sustainable operation of renewable energy -based microgrids, particularly under the inherent variability of solar and wind resources. This study presents a comparative analysis of traditional artificial intelligence models, including Artificial Neural Networks (ANN), Fuzzy Logic, and Adaptive Neuro-Fuzzy Inference Systems (ANFIS), alongside advanced deep learning architectures such as Long Short-Term Memory (LSTM), Temporal Convolutional Networks (TCN), Transformer, and Squeeze-and-Excitation enhanced LSTM (SE+LSTM). Real-world hourly data collected from the King Saud University microgrid in Riyadh, Saudi Arabia --incorporating environmental variables such as solar irradiance, wind speed, temperature, humidity, and air pressure --are used for model training and evaluation. Forecasting performance is assessed using Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Sum of Squared Errors (SSE) across short-term (1-hour ahead) and mid-term (6-hour ahead) forecasting horizons. The results show that attention-based models outperform conventional approaches. In particular, the SE+LSTM model achieves the best performance with an RMSE of 0.7015 kW and a MAPE of 2.01%, followed closely by the Transformer model. Statistical significance testing confirms that the observed improvements are not due to random variation. Overall, the findings highlight the importance of incorporating environmental context to improve forecasting accuracy. Attention-enhanced deep learning models provide a robust and environmentally informed framework for intelligent and sustainable microgrid energy management.