An intelligent switching algorithm based on demand forecasting

Abstract

The increasing complexity of balancing grid power with renewable energy sources, coupled with rising global energy demand, necessitates innovative intelligent switching algorithms. This project focuses on developing an intelligent switching algorithm that leverages advanced forecasting techniques to optimize energy source utilization, enhance efficiency, and reduce costs. By utilizing an Autoregressive Integrated Moving Average (ARIMA) model, the algorithm accurately predicts energy demand based on historical consumption data. The proposed algorithm dynamically switches between grid power, and solar energy based on demand forecasts and real-time solar availability. This prioritization of renewable energy minimizes reliance on the grid, reducing operational costs. The algorithm's framework integrates ARIMA with machine learning for improved adaptability and accuracy in managing non-linear demand fluctuations. Aligned with Uganda’s Vision 2040 and global Sustainable Development Goal 7, the project aims to address inefficiencies in existing intelligent switching algorithms, which rely on static and costly grid-dominant models. The outcomes of this project include cost reductions, improved energy efficiency, and a scalable, sustainable framework for optimizing renewable energy integration in Uganda’s energy landscape.