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.
Highlights
Logistics operations consume significant energy resources worldwide through service processes, transportation, and buildings
We adopted the root mean squared error (RMSE), mean absolute percent error (MAPE), and mean absolute error (MAE) to evaluate and compare the performances of different models as they are the most commonly metrics used to evaluate the accuracy of energy consumption models [21]
When comparing the results obtained with the short-term load forecasting (STLF) and very short-term load forecasting (VSTLF) models, we observed a maximum increase in 177% and 202% in RMSE and MAE, respectively
Summary
Logistics operations consume significant energy resources worldwide through service processes, transportation, and buildings. Meeting the United Nations (UN) Sustainable Development Goals and European Union (EU) targets will require the sector to reduce fossil fuel use and CO2 emissions by over 50% by 2050 [3]. To achieve such targets requires energy efficiencies across the entire logistics system including procurement, warehousing, transportation, production, sales, and information systems [2]. While the relative percentage of greenhouse gases (GHGs) from logistics facilities is small at 0.55%, this still represents over 300 megatonnes of GHG emissions per year [7] This is salient because electricity costs and usage can be dramatically reduced through a number of small interventions [5]. Green warehousing can be a significant first step towards both net zero warehousing and supply chain decarbonisation
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