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

Dimitrios K. Panagiotou and Anastasios I. Dounis ()
Additional contact information
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

Sustainability, 2023, vol. 15, issue 10, 1-21

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.

Keywords: electricity price prediction; adaptive neuro-fuzzy system; fuzzy trees; genetic algorithms; healthcare facilities; sustainable hospitals (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/10/8419/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/10/8419/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:10:p:8419-:d:1152951

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().