A Comparative Study of Single and Multi-Stage Forecasting Algorithms for the Prediction of Electricity Consumption Using a UK-National Health Service (NHS) Hospital Dataset

Ahmad Taha (), Basel Barakat (), Mohammad M. A. Taha, Mahmoud A. Shawky, Chun Sing Lai, Sajjad Hussain, Muhammad Zainul Abideen and Qammer H. Abbasi
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Ahmad Taha: James Watt School of Engineering, College of Science and Engineering, University of Glasgow, Glasgow G12 8QQ, UK
Basel Barakat: School of Computer Science, University of Sunderland, Sunderland SR6 0DD, UK
Mohammad M. A. Taha: Independent Researcher, Dover, NH 03820, USA
Mahmoud A. Shawky: James Watt School of Engineering, College of Science and Engineering, University of Glasgow, Glasgow G12 8QQ, UK
Chun Sing Lai: Brunel Interdisciplinary Power Systems Research Centre, Brunel University London, London UB8 3PH, UK
Sajjad Hussain: James Watt School of Engineering, College of Science and Engineering, University of Glasgow, Glasgow G12 8QQ, UK
Muhammad Zainul Abideen: James Watt School of Engineering, College of Science and Engineering, University of Glasgow, Glasgow G12 8QQ, UK
Qammer H. Abbasi: James Watt School of Engineering, College of Science and Engineering, University of Glasgow, Glasgow G12 8QQ, UK

Future Internet, 2023, vol. 15, issue 4, 1-17

Abstract: Accurately looking into the future was a significantly major challenge prior to the era of big data, but with rapid advancements in the Internet of Things (IoT), Artificial Intelligence (AI), and the data availability around us, this has become relatively easier. Nevertheless, in order to ensure high-accuracy forecasting, it is crucial to consider suitable algorithms and the impact of the extracted features. This paper presents a framework to evaluate a total of nine forecasting algorithms categorised into single and multistage models, constructed from the Prophet, Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and the Least Absolute Shrinkage and Selection Operator (LASSO) approaches, applied to an electricity demand dataset from an NHS hospital. The aim is to see such techniques widely used in accurately predicting energy consumption, limiting the negative impacts of future waste on energy, and making a contribution towards the 2050 net zero carbon target. The proposed method accounts for patterns in demand and temperature to accurately forecast consumption. The Coefficient of Determination ( R 2 ), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) were used to evaluate the algorithms’ performance. The results show the superiority of the Long Short-Term Memory (LSTM) model and the multistage Facebook Prophet model, with R 2 values of 87.20 % and 68.06 % , respectively.

Keywords: artificial intelligence; energy forecasting; energy management; electrical demand forecasting; hospital; National Health Service; net zero carbon target (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2023
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