Electricity Load Prediction using AI

Electricity infrastructure, network, and availability play a significant role in a nation's economic growth, as it is essential for daily life in today's world. As a result, the need for electricity for home and commercial uses has increased dramatically on a global scale. Conversely, power costs have been fluctuating over the past few years, and this is without even addressing the fact that there is not enough power produced to meet demand worldwide. Several studies have been conducted to estimate the future demand for electrical energy for household and commercial applications in order to help providers, distributors, and generators of electricity make effective plans ahead of time and encourage energy conservation among consumers. However, since the beginning of the electricity sector, one of the main issues with load forecasting. Using the potent XGBoost algorithm, This application provides complete solutions for short-term power forecasting. The current system helps in importance of distributed energy sources and smart grids in relation to home energy management systems (HEMS), which act as a central point of contact for residential consumers and utilities to monitor and control energy use. The "Load Dataset Module," which gathers and arranges historical power load data, weather factors, holidays, and pertinent aspects, is the first of the system's many modules, which uses splitting, feature engineering, transformation, and cleaning to guarantee the quality of the data. Influential input features are identified using the "Feature Selection Module". The "Electricity Load Prediction Using XGBoost Module," which is the system's central component, uses pre-processed data to train an XGBoost in order to anticipate load.