Load Forecasting of Supermarket Refrigeration

TL;DR

This study develops adaptive linear time series models with regime switching and spline functions to accurately forecast supermarket refrigeration loads using weather and load data, demonstrating effective handling of non-linearities.

Contribution

The paper introduces a regime switching model combined with spline functions for non-linear load forecasting in supermarket refrigeration, validated with real-world data.

Findings

Spline functions effectively model non-linear relations.

Auto-regressive noise models improve residuals.

Models accurately forecast 42-hour ahead loads.

Abstract

This paper presents a study of models for forecasting the electrical load for supermarket refrigeration. The data used for building the models consists of load measurements, local climate measurements and weather forecasts. The load measurements are from a supermarket located in a village in Denmark. Every hour the hourly electrical load for refrigeration is forecasted for the following 42 hours. The forecast models are adaptive linear time series models. The model has two regimes; one for opening hours and one for closing hours, this is modelled by a regime switching model and two different methods for predicting the regimes are tested. The dynamic relation between the weather and the load is modelled by simple transfer functions and the non-linearities are described using spline functions. The results are thoroughly evaluated and it is shown that the spline functions are suitable for handling the non-linear relations and that after applying an auto-regressive noise model the one-step ahead residuals do not contain further significant information.