Optimal activity and battery scheduling algorithm using load and solar generation forecasts

TL;DR

This paper presents a joint approach for energy forecasting and optimal scheduling to reduce electricity costs, focusing on solar generation and building demand predictions for practical applications.

Contribution

It introduces a combined methodology for solar and demand forecasting alongside an optimal scheduling algorithm tailored for building energy management.

Findings

Effective solar and demand forecasting methods developed

Optimized scheduling reduces energy costs in practical scenarios

Demonstrated applicability in IEEE-CIS competition context

Abstract

Energy usage optimal scheduling has attracted great attention in the power system community, where various methodologies have been proposed. However, in real-world applications, the optimal scheduling problems require reliable energy forecasting, which is scarcely discussed as a joint solution to the scheduling problem. The 5\textsuperscript{th} IEEE Computational Intelligence Society (IEEE-CIS) competition raised a practical problem of decreasing the electricity bill by scheduling building activities, where forecasting the solar energy generation and building consumption is a necessity. To solve this problem, we propose a technical sequence for tackling the solar PV and demand forecast and optimal scheduling problems, where solar generation prediction methods and an optimal university lectures scheduling algorithm are proposed.