Demand response for efficient power generation in smart grids using hyper–local weather prediction

Optimizing power generation is crucial in the evolving smart grid and urban ecosystem landscape. Efficient energy use is vital in today’s tech-driven world, with smart grids leading the way in managing energy consumption and distribution. Given the link between climate and energy use, integrating weather data is now essential. This research presents a method combining demand response in smart grids with hyperlocal weather predictions using machine learning to enhance power generation forecasts. Five models—SARIMAX, Prophet, XGBoost, LSTM, and Holt–Winters are trained on the smart grid and weather data, including precipitation, humidity, temperature, and cloud cover. Extensive feature engineering, such as lag terms and cyclic encoding, improved accuracy. SARIMAX outperformed others with an MAE of 3.54 and an R-squared of 0.97, while the hybrid model, combining SARIMAX, Prophet, and Holt–Winters, achieved an R-squared of 0.96. The hybrid model’s dynamic recalibration improved accuracy with new data. A real-time dashboard is also developed, offering dynamic, user-specific forecasting insights. This research enhances smart grid management by integrating machine learning and demand response for more efficient energy use.