Weather-integrated machine learning and deep learning framework for long-term solar photovoltaic forecasting in Oman
Accurate long-term solar photovoltaic (PV) power forecasting is essential for renewable energy planning, grid stability, and sustainable energy transition, particularly in regions with high solar potential and climatic variability. This study proposes a weather-integrated hybrid machine learning (ML) and deep learning (DL) framework for long-term solar PV forecasting in Oman using a decade-long daily meteorological dataset (2014–2024) collected from three geographically diverse locations: Izki, Muscat, and Sadah. The framework integrates meteorological variables, including solar radiation, temperature, humidity, rainfall, wind speed, vapor pressure, sunshine duration, and atmospheric pressure. Data preprocessing included missing-value imputation, anomaly correction, normalization, seasonal decomposition, stationarity testing, and cyclical feature engineering to improve forecasting robustness. Conventional statistical approaches (autoregressive integrated moving average, seasonal autoregressive integrated moving average, and exponential smoothing), ML algorithms, and hybrid DL architectures were comparatively evaluated. Results indicate that ensemble regression achieved superior ML performance with validation R2 = 0.96, root mean squared error = 38.60, and mean absolute percentage error = 6.16%, while the recursive long short-term memory + convolutional neural network model with residual correction demonstrated enhanced stability for long-term forecasting horizons. Explainability analysis using Shapley additive explanations and partial dependence plots identified latitude, seasonal encodings, sunshine duration, and atmospheric variables as dominant predictors. The proposed framework provides an interpretable and scalable forecasting solution for renewable energy planning in Oman and similar climatic regions.
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