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Title: 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
Authors: Taha, A
Barakat, B
Taha, MMA
Shawky, MA
Lai, CS
Hussain, S
Abideen, MZ
Abbasi, QH
Keywords: artificial intelligence;energy forecasting; energy management;electrical demand forecasting;hospital;National Health Service;net zero carbon target
Issue Date: 31-Mar-2023
Publisher: MDPI
Citation: Taha, A. et al. (2023) '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', Future Internet, 15 (4), 134, pp.1 - 17. doi: 10.3390/fi15040134.
Abstract: Copyright © 2023 by the authors. 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 (R2), 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 R2 values of 87.20% and 68.06%, respectively.
Description: Data Availability Statement: Restrictions apply to the availability of the electricity consumption data. The data belong to Medway NHS Foundation Trust but were collected using systems provided by EnergyLogix. Data, however, can be made available with the approval of the corresponding author (A.T.), Medway NHS Foundation Trust, and EnergyLogix. As for the weather data, they were obtained from [24].
Other Identifiers: ORCID iDs: Ahmad Taha, Basel Barakat; Mohammad M. A. Taha; Mahmoud A. Shawky; Chun Sing Lai; Sajjad Hiussain; Muhammad Zainul Abideen; Qammer H. Abbasi
Appears in Collections:Dept of Electronic and Electrical Engineering Research Papers

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