Article
MACHINE LEARNING-BASED ENERGY CONSUMPTION FORECASTING IN SMART GRIDS
The integration of machine learning-based energy consumption forecasting into smart grids offers significant benefits across various domains. In the utility sector, accurate forecasts enable optimized energy generation, load balancing, and infrastructure planning, resulting in improved operational efficiency and cost reductions. For renewable energy sources, such forecasting enhances the integration of intermittent resources like solar and wind, supporting grid stability and sustainability. In smart buildings and homes, energy forecasts empower users to manage consumption proactively, improve comfort, and reduce electricity bills. Traditional forecasting methods, such as statistical models and time series analysis, often fall short in handling the complexity of smart grid data. These methods typically depend on manual feature selection and struggle to incorporate contextual factors like weather, holidays, and user behavior, leading to reduced forecast accuracy and scalability limitations. To address these challenges, the proposed system employs machine learning algorithms that learn from diverse features—temporal data, environmental conditions, and grid characteristics— to provide more accurate and dynamic forecasts. Regression models are used to uncover complex patterns, while ensemble learning and optimization techniques further boost performance. This approach offers a robust, scalable solution for modern energy management needs.
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