EcoLoad

EcoLoad - Cutting waste, powering efficiency

Abstract

Energy demand is becoming increasingly unpredictable due to rapid urbanization and renewable energy adoption. This unpredictability causes grid inefficiency, energy waste, and even blackouts. Our project, EcoLoad, addresses this challenge by providing AI-driven short-term energy load forecasting. EcoLoad combines historical electricity usage data, weather patterns, and calendar events to generate accurate short-term demand forecasts (hourly/daily). By predicting demand peaks and troughs, EcoLoad enables utilities to balance supply, prevent outages, and integrate renewable energy sources more efficiently. We propose to develop a forecasting model using time-series techniques (LSTM and then XGBoost), evaluate performance with metrics such as MAPE and RMSE, and deploy results through a dashboard interface for real-time insights. The solution will reduce energy waste, cut operational costs, and support sustainability goals.

Built With

• fastapi
• numpy
• pandas
• python
• scikit-learn
• tensorflow
• xgboost