Author: Dr. Rakesh Sharma, Er. Priya Nair.
Abstract: Load forecasting is a critical component in modern power system planning and operation. Accurate forecasting methods ensure optimal generation scheduling, minimize operational costs, and enhance system reliability. This paper presents an overview of traditional, statistical, and machine learning-based load forecasting methods, highlighting their strengths, limitations, and practical applications in electrical networks. The evolution of forecasting techniques is discussed in the context of increasing renewable energy integration and demand-side management. Comparative analysis and optimization strategies are provided to guide engineers in selecting suitable methods for specific operational requirements.
Keywords: Load forecasting, electrical networks, time series analysis, neural networks, demand prediction, renewable integration.
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