The Effect of Input Length on Prediction Accuracy in Short-Term Multi-Step Electricity Load Forecasting Using CNN-LSTM

Project Code :TCMAPY2324

Objective

The project aims to analyze how varying input sequence lengths affect prediction accuracy in short-term multi-step electricity load forecasting. By utilizing a hybrid CNN-LSTM model, the system captures both spatial and temporal dependencies in energy consumption data. The model is trained on historical load data to forecast future demand efficiently. This helps improve energy management, reduce power wastage, and support smart grid systems by enabling accurate demand prediction.

Abstract

This project investigates the effect of input sequence length on the accuracy of short-term multi-step electricity load forecasting using deep learning techniques. Using a real-world dataset from Panama's electricity consumption records, we implemented and compared the performance of four model: CNN-LSTM with Dense layers . This model is evaluated for both hourly and daily forecasting to determine the optimal input configuration and architecture for high-precision predictions. CNN-LSTM model demonstrated superior performance and was selected for deployment. To enable practical usage, we developed a Flask-based web application with an intuitive front end using HTML, CSS, and JavaScript. After user login, the application allows users to input specific dates and receive accurate load forecasts along with interactive graphs. This system provides a reliable and user-friendly platform for electricity demand forecasting, supporting better planning and decision-making in energy management.

Keywords:
Short-term load forecasting, CNN-LSTM, electricity demand, input length, deep learning, Flask web app, time series prediction, energy forecasting.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.