Short- and Very Short-Term Firm-Level Load Forecasting for Warehouses: A Comparison of Machine Learning and Deep Learning Models

Andrea Maria N. C. Ribeiro, Pedro Rafael X. do Carmo, Patricia Takako Endo, Pierangelo Rosati and Theo Lynn
Additional contact information
Andrea Maria N. C. Ribeiro: Centro de Informática, Universidade Federal de Pernambuco, Recife 50670-420, Brazil
Pedro Rafael X. do Carmo: Centro de Informática, Universidade Federal de Pernambuco, Recife 50670-420, Brazil
Patricia Takako Endo: Programa de Pós-Graduação em Engenharia da Computação Pernambuco, Universidade de Pernambuco, Recife 50050-000, Brazil
Pierangelo Rosati: Irish Institute of Digital Business, Dublin City University, Collins Avenue, D09 Y5N0 Dublin, Ireland
Theo Lynn: Irish Institute of Digital Business, Dublin City University, Collins Avenue, D09 Y5N0 Dublin, Ireland

Energies, 2022, vol. 15, issue 3, 1-24

Abstract: Commercial buildings are a significant consumer of energy worldwide. Logistics facilities, and specifically warehouses, are a common building type which remain under-researched in the demand-side energy forecasting literature. Warehouses have an idiosyncratic profile when compared to other commercial and industrial buildings with a significant reliance on a small number of energy systems. As such, warehouse owners and operators are increasingly entering energy performance contracts with energy service companies (ESCOs) to minimise environmental impact, reduce costs, and improve competitiveness. ESCOs and warehouse owners and operators require accurate forecasts of their energy consumption so that precautionary and mitigation measures can be taken. This paper explores the performance of three machine learning models (Support Vector Regression (SVR), Random Forest, and Extreme Gradient Boosting (XGBoost)), three deep learning models (Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)), and a classical time series model, Autoregressive Integrated Moving Average (ARIMA) for predicting daily energy consumption. The dataset comprises 8040 records generated over an 11-month period from January to November 2020 from a non-refrigerated logistics facility located in Ireland. The grid search method was used to identify the best configurations for each model. The proposed XGBoost models outperformed other models for both very short-term load forecasting (VSTLF) and short-term load forecasting (STLF); the ARIMA model performed the worst.

Keywords: very short-term load forecasting; VSTLF; short-term load forecasting; STLF; deep learning; RNN; LSTM; GRU; machine learning; SVR; Random Forest; Extreme Gradient Boosting; energy consumption; ARIMA; time series prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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