Temporal Convolutional Networks for Multi-Horizon Time Series Forecasting in Renewable Energy
Abstract
Accurate forecasting of renewable energy generation is critical for grid stability and resource planning. This study investigates the application of temporal convolutional networks (TCNs) for multi-horizon time series forecasting in solar and wind energy datasets. The proposed model captures long-term dependencies and temporal patterns, outperforming traditional models like ARIMA and LSTMs. Results from real-world datasets indicate substantial improvements in forecasting accuracy, making TCNs a promising tool for renewable energy management.
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