Data Mining Techniques for Predicting Energy Demand in Smart Grids: Optimizing Consumption and Production

The growing complexity of renewable energy systems and the smarter grid has also made energy management a major issue. Precise forecasting of energy demand remains one of the most critical factors for effective energy utilization and generation, concern, dependability, and cost-efficiency. In this research, we present state-of-art data mining methods for predicting the energy demand in smart grids. In view of this, this study uses decision tree, support vector machines and neural network to establish models from historical energy consumption data as well as weather and user behavior patterns to predict the consumption accurately. The authors proposed architecture is meant to work with large streams of data in real-time to support dynamic control of energy loads and other demand response methodologies. Additionally, the research measures the effectiveness of feature reduction, dimension reduction, and clustering in enhancing the energy plans' predictive models. These methods do not only improve predictive capability but also help in designing energy distribution and storage systems. The results emphasize on the necessity of context-specific data mining solutions for digital smart grids, managing their specific problems. This study will be useful to energy suppliers and governments to build foundations for effective and practicable energy utilization in the smart grid environment.