Explainable Hybrid CNN-LSTM Model for Renewable Energy Forecasting to Enable Carbon Emission Reduction
TL;DR
A hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) model for forecasting multi-source renewable energy, which achieves lower prediction error and can contribute to reduced renewable curtailment and to more stable, reasonable decisions in smart cities.
Abstract
The integration of renewable energy, such as solar and wind power, is essential for the development of zero-carbon smart cities. Reliable short-term and medium-term predictions help reduce dependence on fossil-fuel reserves and facilitate more reasonable decisions in urban systems. This study develops a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) model for forecasting multi-source renewable energy. The convolutional layers can extract local temporal patterns from meteorological variables, while the LSTM layers model sequential dependencies over longer horizons. To improve transparency, Local Interpretable Model-agnostic Explanations (LIME) is applied to examine how input variables influence predictions. Experiments are conducted using data from the National Renewable Energy Laboratory, including the National Solar Radiation Database and the Wind Integration National Dataset. Compared with standalone CNNs, LSTMs, and conventional learning models, the hybrid model achieves lower prediction error, with Mean Absolute Percentage Error typically below 3% for forecasting next-day values. Results indicate that higher forecasting accuracy can contribute to reduced renewable curtailment and to more stable, reasonable decisions in smart cities. The proposed framework offers a practical, interpretable approach to renewable energy forecasting in urban systems.