Day-ahead natural gas demand forecasting based on the combination of wavelet transform and ANFIS/genetic algorithm/neural network model

• Published in: Energy (Oxford) Vol. 118; pp. 231 - 245
• Main Authors: Panapakidis, Ioannis P., Dagoumas, Athanasios S.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.01.2017; Elsevier BV
• Subjects: Adaptive algorithms; Adaptive systems; Algorithms; Artificial neural networks; Computational intelligence; Consumption patterns; decision making; Demand; Economic forecasting; Electric power generation; energy; Energy consumption; Fuzzy inference; Fuzzy logic; Fuzzy systems; Genetic algorithms; Hybrid systems; Industrial areas; Intelligence; Mathematical models; Natural gas; Natural gas demand forecasting; Neural networks; power generation; prediction; Public transportation; Robustness (mathematics); Service stations; Supply-demand forecasting; Towns; Urban areas; Urban environments; Utilities; wavelet; Wavelet transforms; Natural gas demand forecasting; Fuzzy inference; Computational intelligence; Artificial neural networks; Genetic algorithms
• ISSN: 0360-5442; 1873-6785
• DOI: 10.1016/j.energy.2016.12.033

Accurate forecasts of natural gas demand can be essential for utilities, energy traders, regulatory authorities, decision makers and others. The aim of this paper is to test the robustness of a novel hybrid computational intelligence model in day-ahead natural gas demand predictions. The proposed model combines the Wavelet Transform (WT), Genetic Algorithm (GA), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Feed-Forward Neural Network (FFNN). The WT is used to decompose the original signal in a set of subseries and then a GA optimized ANFIS is employed to provide the forecast for each subseries. ANFIS output is fed into a FFNN to refine the initial forecast and upgrade the overall forecasting accuracy. The model is applied to all distribution points that compose the natural gas grid of a country, in contradiction to the majority of the literature that focuses on a limited number of distribution points. This approach enables the comparison of the model performance on different consumption patterns, providing also insights on the characteristics of large urban centers, small towns, industrial areas, power generation units, public transport filling stations and others. •The paper focuses in natural gas demand forecasting.•A novel hybrid computational intelligence model is proposed.•A large number of natural gas distribution points is examined.