Abstract
Function of Building control and analytics, as well as grid-interactive and energy-efficient building operations, rely on building load prediction. Furthermore, electric transmission lines must be sized for a specific maximum power, and overloading will lead towards blackouts or electrical mishaps. As we move closer to a smart grid system, short-term load forecasting has become even more important due to the increasing share of temperature dependent renewable energy sources. . In this work, we proposed a load forecast through the decomposition of the time series using python programming. Many computational models were compared with three different regression models, and the results exhibited the proposed work have improved results with enhanced accuracy levels compared with existing works along with way paves to solve peak load record problem.
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