Smart Energy Forecasting: Harnessing Machine Learning for Consumption Prediction

Abstract

Objective: Predicting energy consumption accurately is essential for efficient resource management, cost savings, and advancing sustainable energy practices. Traditional statistical methods often fall short when dealing with the complex and dynamic nature of energy usage patterns. This study focuses on developing a reliable machine learning (ML) model capable of delivering high-accuracy energy consumption forecasts. Methodology: We introduce a hybrid approach that integrates IoT-based data collection with advanced ML algorithms. Using smart meters, we gather historical energy consumption (in kW) and cost data (in INR), which is transmitted to the ThingSpeak cloud platform. The data is pre-processed through normalization and feature engineering before being used to train several ML models, including Linear Regression, Random Forest, and Long Short-Term Memory (LSTM) networks. The LSTM model is particularly suited for time-series forecasting due to its strength in capturing temporal patterns and dependencies. Results: Among all the models tested, the LSTM model delivers the best performance, achieving a Root Mean Square Error (RMSE) of 0.23 and a Mean Absolute Error (MAE) of 0.18. Its real-time predictions closely track actual usage, including during periods of peak demand. The system also features a working hardware prototype using Arduino and ESP8266, which supports seamless data logging and visualization. Conclusion: Our findings highlight the potential of LSTM networks in providing accurate and scalable energy consumption forecasts across residential, commercial, and industrial settings. Future developments will focus on integrating renewable energy inputs and leveraging edge computing to enable localized, real-time predictions.