Development of a Predictive Model for Monthly Electricity Consumption Using Population and Weather Data with Web-Based Programming Language

Predicting accurate electricity consumption on electrical distribution network is essential for efficient energy management, particularly in institutional settings where demand fluctuates due to population growth and weather variations. Traditional prediction models often lack real-time accessibility. This study presents a browser-based simulation model for monthly electricity consumption using historical data and dynamic weather inputs, implemented with HTML, CSS, and JavaScript. The model generates forecasts by applying statistically plausible variations (±5%) to historical consumption patterns, integrated with simulated weather data for real-time scenario testing. Compared to complex machine learning approaches, this lightweight solution offers enhanced scalability, remote accessibility, and instant updates without server dependencies which makes it more applicable for smart grid systems and utility management. Results demonstrate its utility as a practical tool for preliminary energy trend analysis, supporting integration with cloud-based data sources. This research will contribute to accessible energy forecasting tools and provides a practical tool for optimizing electricity consumption on electrical distribution network in institutional environments and for institutional planning.