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An ANFIS-Fuzzy Tree-GA Model for a Hospital’s Electricity Purchasing Decision-Making Process Integrated with Virtual Cost Concept

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  • Dimitrios K. Panagiotou

    (Department of Biomedical Engineering, University of West Attica, Egaleo Park Campus, 12243 Athens, Greece)

  • Anastasios I. Dounis

    (Department of Biomedical Engineering, University of West Attica, Egaleo Park Campus, 12243 Athens, Greece)

Abstract

In deregulated electricity markets, accurate load and price prediction play an essential role in the Demand Response (DR) context. Although electrical load and price demonstrate a strong correlation which is not linear, price prediction may be a task much more challenging than load prediction due to several factors. The volatility of electricity price compared to load makes price prediction a complex procedure. To perform purchasing decisions commercial consumers may rely on short term price and load prediction. A system which combines Adaptive Neuro-Fuzzy Systems (ANFIS) which predict Load Marginal Prices (LMPs) and electricity consumption is presented in this study. Furthermore, the Virtual Cost (VC) concept, which is the sum of the products between the predicted hourly consumption values and their respective predicted LMPs is introduced. Virtual Cost is assessed with a Fuzzy Decision Tree (FDT) compared to a threshold set by the customer. If needed, the amount of electrical energy that a healthcare facility must purchase at every hour of the day may be scheduled using Genetic Algorithm (GA) to meet the threshold criterion. This hybrid model proved economically beneficial for the facility, which is of great importance since the saved resources may be utilized to improve its infrastructures or for other purposes with social impact. The novelty of the proposed method is the utilization of ANFIS, Fuzzy Decision Trees and Genetic Algorithms combined as tools to improve the hospital’s energy and economic efficiency, achieving a reduction of the electricity costs up to 21.95 percent. The contribution of the study is to provide a reliable decision-making tool to everyone who participates in the electricity market in order to perform profitable energy scheduling automatically and accurately.

Suggested Citation

  • Dimitrios K. Panagiotou & Anastasios I. Dounis, 2023. "An ANFIS-Fuzzy Tree-GA Model for a Hospital’s Electricity Purchasing Decision-Making Process Integrated with Virtual Cost Concept," Sustainability, MDPI, vol. 15(10), pages 1-21, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:10:p:8419-:d:1152951
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    References listed on IDEAS

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    1. George Kyriakarakos & Anastasios Dounis, 2020. "Intelligent Management of Distributed Energy Resources for Increased Resilience and Environmental Sustainability of Hospitals," Sustainability, MDPI, vol. 12(18), pages 1-4, September.
    2. Ogunjuyigbe, A.S.O. & Ayodele, T.R. & Akinola, O.A., 2017. "User satisfaction-induced demand side load management in residential buildings with user budget constraint," Applied Energy, Elsevier, vol. 187(C), pages 352-366.
    3. Khaled Bawaneh & Farnaz Ghazi Nezami & Md. Rasheduzzaman & Brad Deken, 2019. "Energy Consumption Analysis and Characterization of Healthcare Facilities in the United States," Energies, MDPI, vol. 12(19), pages 1-20, October.
    4. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
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