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

TL;DR

This paper presents a forecast-based optimal scheduling algorithm for solar PV and demand, integrating load prediction and battery management to minimize energy costs and achieve net-zero grid import.

Contribution

It introduces a novel load and solar generation forecasting approach combined with an optimization framework for activity and battery scheduling.

Findings

Accurate load and solar forecasts improve scheduling efficiency.

The optimization reduces total energy costs and grid dependency.

The method successfully achieves net-zero energy import.

Abstract

In this report, we provide a technical sequence on tackling the solar PV and demand forecast as well as optimal scheduling problem proposed by the IEEE-CIS 3rd technical challenge on predict + optimize for activity and battery scheduling. Using the historical data provided by the organizers, a simple pre-processing approach with a rolling window was used to detect and replace invalid data points. Upon filling the missing values, advanced time-series forecasting techniques, namely tree-based methods and refined motif discovery, were employed to predict the baseload consumption on six different buildings together with the power production on their associated solar PV panels. An optimization problem is then formulated to use the predicted values and the wholesale electricity prices to create a timetable for a set of activities, including the scheduling of lecture theatres and battery charging and discharging operation, for one month ahead. The valley-filling optimization was done across all the buildings with the objective of minimizing the total energy cost and achieving net-zero imported power from the grid.