Kistipati Veera Venkata Maheswara Reddy
The increasing demand for electricity has created a need for accurate and reliable electricity demand forecasting to support efficient power generation, distribution, and energy management. Electricity consumption varies according to time, seasonal patterns, weekdays, weekends, weather conditions, and peak-demand periods, making accurate prediction a challenging task. This paper presents an Electricity Demand Prediction Using Time Series Analysis web application designed to provide a centralized and user-friendly platform for analyzing historical electricity demand and predicting future consumption. The system enables users to upload electricity demand datasets, preprocess and analyze the data, identify demand patterns, generate future forecasts, evaluate prediction performance, and visualize results through interactive dashboards. The application processes real-world and historical electricity datasets and applies time-series forecasting and machine-learning techniques to predict future electricity demand. Interactive charts and statistical visualizations are used to represent demand trends, peak-demand periods, actual versus predicted values, and model performance. The proposed system helps users understand electricity consumption patterns, improve demand forecasting, support energy-management decisions, and demonstrate how modern data analytics and predictive technologies can be applied to electricity demand forecasting.
DOI has been requested and is pending allotment.