Electricity Consumption Forecasting Project Guide

Accurately forecasting electricity consumption is critical for grid management, reducing power wastage, and ensuring supply meets demand. Sudden demand surges can overload systems, while overproduction leads to wastage. Consumption patterns are influenced by seasonality, weather, industrial cycles, and lifestyle changes, making accurate forecasting a complex challenge. Solving it requires time series analysis, multivariate modeling, and robust evaluation strategies for effective real-world deployment.

Using historical electricity usage data combined with external variables like temperature and holidays, machine learning models and time series forecasting techniques can predict future consumption levels. Models like ARIMA, Prophet, XGBoost Regression, and LSTM neural networks can capture trends, seasonality, and cyclic behavior. Implementing an energy demand forecasting system teaches vital skills in time series preprocessing, trend decomposition, feature engineering, and operational deployment for smart energy management.

The system collects hourly or daily electricity consumption data along with external factors like temperature, humidity, holidays, and workday indicators. After cleaning and feature engineering, time series forecasting models are trained to predict future consumption values. Hybrid models combining ARIMA for trend/seasonality and LSTM for complex nonlinear patterns often yield the best results. Deploying forecasts on dashboards allows utility companies and businesses to plan power generation and manage loads dynamically.

• Collect datasets of historical electricity consumption and weather/environmental conditions.
• Preprocess data: resample time series, fill missing values, encode seasonality variables like months and holidays.
• Train models like ARIMA, Prophet, or LSTM to capture consumption patterns and forecast future values.
• Evaluate using metrics like RMSE, MAE, MAPE (Mean Absolute Percentage Error) for model accuracy assessment.
• Deploy forecasts on interactive dashboards with alerting features for peak demand prediction.

• UCI Individual Household Electricity Dataset
• Facebook Prophet Time Series Library
• TensorFlow LSTM Time Series Tutorial