Demand Response for Efficient Power Generation in Smart Grids Using Hyperlocal Weather Predictions

Abstract

Optimising power-generation efficiency has arisen as a critical challenge in the fastchanging environment of smart grids and urban ecosystems. Properly using energy resources has become essential in today’s technologically advanced world. Smart Grids are at the forefront of this shift, giving a complete energy consumption and distribution approach. Given the delicate connection between climatic conditions and energy use patterns, including weather data in these networks is an improvement and a necessity now. Significant advances in the domain have used large amounts of weather data and sophisticated models. Future studies can improve on these findings by combining sophisticated time series models with meteorological data and optimising them for demand response techniques in smart grid power generation. This study presents a novel strategy combining smart grid electricity generation demand response mechanisms with hyperlocal weather forecasts. The project attempts to improve the accuracy and reliability of power generation estimates by using the capability of machine learning. Five distinct time series and machine learning models - SARIMAX, Prophet, Holt-Winters, XGBoost, and LSTM – have been integrated with hyperlocal meteorological data, encompassing precipitation, relative humidity, temperature, and cloud cover. SARIMAX has shone out among the individual models, with a MAPE of 4.92% and an MAE of 3.54, demonstrating its ability to capture subtle seasonal patterns and autocorrelations. The hybrid model, an ensemble of SARIMAX, Prophet, and Holt-Winters, outperformed the individual SARIMAX in predicted accuracy, boasting an impressive 0.06% MAPE and an MAE of 4.43. When paired with real-time data analytics, this demonstrates the transformational potential of machine learning algorithms. A new aspect of this research is introducing a user-centric dashboard, which provides a real-time display of anticipated data and model performance indicators. Its versatility is enhanced further by user-specific customisation options, which provide specialised forecasting insights over user-defined timeframes, increasing real-time decision-making processes. The combination of demand response tactics with powerful machine learning models, demonstrated by the hybrid model’s excellent performance, offers a promising path toward increased flexibility and efficiency in smart grids and cities.