Authors - Dheekshitha Bazar, Gajelli Sai Susmitha, Shreshta Myana, Ramu Kuchipudi, Ramakrishna Kolikipogu, P. Ramesh Babu, K. Gangadhara Rao Abstract - Strategic planning, grid management, and lessening the financial burden on Telangana’s power sector all depend on accurate demand forecasts for electricity. Currently, forecasting methods rely primarily on traditional approaches, but these models often fall short in capturing complex demand patterns at multiple time intervals, especially in dynamic sectors like agriculture. Existing forecasting methods, focused mainly on traditional approaches, often fall short in capturing complex demand patterns across multiple time scales, particularly in sectors like agriculture. This study introduces a comprehensive multi-scale forecasting model for Telangana’s electricity consumption over the next five years, targeting yearly, monthly, weekly, and daily intervals, with a focus on peak load forecasting. Time series techniques such as ARIMA, Prophet, Weighted Moving Average (WMA), and Error Trend Seasonality (ETS) are leveraged to capture seasonality, trends, and short-term fluctuations in demand, providing actionable insights for the Telangana SLDC. Methods for machine learning such as Long Short-Term Memory (LSTM) networks and Gradient Boosting Machines (GBM), are integrated to capture complex temporal patterns and improve predictive accuracy. This study offers a scalable framework for electricity demand forecasting, adaptable to other regions and utilities, advancing methodologies in the power sector. The suggested approach uses metrics to assess the model’s performance such as Root Mean Square Error, Mean Absolute Error (MAE), Both Mean Absolute Percentage Error (MAPE) and RMSE are used to choose the most precise model for every period.
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