Machine Learning for Renewable Energy Forecasting and Smart Grid Optimization

Abstract

The rapid integration of variable renewable energy resources (VREs), with the most prominent examples being solar photovoltaic and wind power, into modern power systems has dramatically changed the operation of electricity networks and has introduced an unprecedented stochasticity to the conventional planning tools. The smart grid has thus evolved from a nice-to-have feature to an essential one: accurate short-term generation forecasts and intelligent dispatch strategies. In this paper, an end-to-end machine learning model is proposed, consisting of a 1D Convolutional Feature Extractor, a Bi-directional Long-Short Term Memory (Bi-LSTM) temporal encoder, and a multi-head self-attention module, to predict solar PV and wind power output at an hourly scale. These forecasts are then fed to a Deep Q-Network (DQN) agent that optimizes the economic dispatch, battery state-of-charge management, and demand-response signaling for a district-scale smart-grid feeder. The framework is trained and tested on two years of high frequency data from multiple sources, which includes 17,520 hourly records for each generation asset, supplemented by weather reanalysis data and grid-side load and price signals. The proposed hybrid model achieves a root-mean-square error (RMSE) of 58.2 kW and coefficient of determination (R²) of 0.972 for solar forecasting and 69.7 kW and 0.965 for wind forecasting under the same experimental conditions, which is a mean improvement of 34.7 % over the best single-architecture baseline and 59.2 % over the classical statistical baselines. On the dispatch side, the DQN-based controller achieves 27.4 % reduction in operational cost, 31.8 % improvement in the grid stability index, and 22.6 % higher renewable absorption without curtailment compared to a rule-based baseline. These gains are statistically significant (paired t-test, p < 0.01) and feature-attribution analysis with SHAP values shows that, as expected from domain knowledge, global horizontal irradiance dominates the first 24 hours of the forecast, and wind speed dominates the final 24 hours. The proposed framework therefore provides a ready-to-deploy blueprint for utility companies looking to bridge the divide between renewable energy generation and consistent grid delivery.

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