Forecasting of Day-Ahead Natural Gas Consumption Demand in Greece Using Adaptive Neuro-Fuzzy Inference System

Abstract

(1) Background: Forecasting of energy consumption demand is a crucial task linked directly with the economy of every country all over the world. Accurate natural gas consumption forecasting allows policy makers to formulate natural gas supply planning and apply the right strategic policies in this direction. In order to develop a real accurate natural gas (NG) prediction model for Greece, we examine the application of neuro-fuzzy models, which have recently shown significant contribution in the energy domain. (2) Methods: The adaptive neuro-fuzzy inference system (ANFIS) is a flexible and easy to use modeling method in the area of soft computing, integrating both neural networks and fuzzy logic principles. The present study aims to develop a proper ANFIS architecture for time series modeling and prediction of day-ahead natural gas demand. (3) Results: An efficient and fast ANFIS architecture is built based on neuro-fuzzy exploration performance for energy demand prediction using historical data of natural gas consumption, achieving a high prediction accuracy. The best performing ANFIS method is also compared with other well-known artificial neural networks (ANNs), soft computing methods such as fuzzy cognitive map (FCM) and their hybrid combination architectures for natural gas prediction, reported in the literature, to further assess its prediction performance. The conducted analysis reveals that the mean absolute percentage error (MAPE) of the proposed ANFIS architecture results is less than 20% in almost all the examined Greek cities, outperforming ANNs, FCMs and their hybrid combination; and (4) Conclusions: The produced results reveal an improved prediction efficacy of the proposed ANFIS-based approach for the examined natural gas case study in Greece, thus providing a fast and efficient tool for utterly accurate predictions of future short-term natural gas demand.

Predicting Australia’s Domestic Airline Passenger Demand using an Anfis Approach

“…The parameters in Layer 1 are called the premise parameters (Faed, 2013;Gomathi et al, 2021) and consequent parameters in Layer 4 (Papageorgiou et al, 2020).…”

Agricultural Cooperatives as a Vehicle for Small-Scale Farmer’s Viability and Sustainable Practices

“…Agricultural cooperatives, when operating well, act as a means of obtaining a wide range of valuable resources, including information, technology, marketing, credit, purchasing power, and equipment (van Dijk et al, 2019) [1] (p. 176). According to Papageorgiou, individuals engaged in farming operations may enjoy numerous advantages by participating in cooperatives, such as reduced production expenses and enhanced managerial practices (Papageorgiou, 2015) [2] (pp. 75-76).…”

Reliable Prediction on Emerging Energy Supply for National Sustainability and Stability: A Case Study on Coal Bed Gas Supply in China Based on the Dual-LSTM Model

“…The imbalance between supply and demand of energy is regarded as the potential impact on the national energy security and operation. Reliable prediction of energy supply is the key to avoid the imbalance of supply and demand [1][2][3][4][5][6]. Once if the predictive value of energy supply was inadequate, it would lead to surplus import.…”