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

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