Issue: 2024/Vol.34/No.3, Pages 165-192
ANALYSING AND FORECASTING THE ENERGY CONSUMPTION OF HEALTHCARE FACILITIES IN THE SHORT AND MEDIUM TERM. A CASE STUDY
Ali Koç
, Serap Ulusam Seçkiner
Cite as: A. Koç, S. U. Seçkiner. Analysing and forecasting the energy consumption of healthcare facilities in the short and medium term. A case study. Operations Research and Decisions 2024: 34(3), 165-192. DOI 10.37190/ord240309
Abstract
Healthcare facilities consist of multiple large buildings with complex energy systems and high energy consumption, resulting in high carbon emissions. The increasing trend in energy consumption of these facilities and the process of selecting an energy supplier from the open market requires reliable and robust energy forecasting studies. This situation calls for the use of reliable and accurate energy consumption prediction models for the energy needs of healthcare buildings. The aim of this study is to present a prediction framework based on historical energy consumption at different time intervals using six supervised regression algorithms, three linear single, one non- linear single and two non-linear ensembles. The approach adopted for predicting hospital energy consumption involves five steps: data acquisition, data pre-processing, data prediction, hyper-parameter optimisation and feature analysis. Furthermore, all regression algorithms have undergone hyper-parameter optimisation using random search, grid search and Bayesian optimisation to achieve the minimum prediction errors represented by different metrics. The results displayed that the two ensemble models, Extreme Gradient Boosting and Random Forest, outperformed single models in hourly, daily, and monthly energy load prediction. Nevertheless, when considering the computational time for all regression models, the single models have better computational times, although the error metrics are not as good as for the ensemble models. In addition, grid search and Bayesian optimisation performed better than random search in finding optimal hyperparameter values for all datasets. Finally, thanks to feature importance analysis, the most influential features under the hourly, daily, and monthly electrical and monthly natural gas prediction were identified.
Keywords: healthcare facilities, electricity, natural gas, consumption, forecasting, machine learning
Received: 4 August 2023 Accepted: 14 July 2024
Published online: 17 October 2024