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Short-Term Electric Power Forecasting Using Dual-Stage Hierarchical Wavelet- Particle Swarm Optimization- Adaptive Neuro-Fuzzy Inference System PSO-ANFIS Approach Based On Climate Change

Author

Listed:
  • Samuel Atuahene

    (Center for Modern Information Management, School of Management, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Yukun Bao

    (Center for Modern Information Management, School of Management, Huazhong University of Science and Technology, Wuhan 430074, China
    Center for Big Data Analytics, Jiangxi University of Engineering, Xinyu 338000, China)

  • Yao Yevenyo Ziggah

    (Department of Geomatic Engineering, University of Mines and Technology, Tarkwa 00233, Ghana)

  • Patricia Semwaah Gyan

    (Faculty of Earth Resources, China University of Geosciences, Wuhan 430074, China)

  • Feng Li

    (Central China Branch, State Grid Corporation of China, Wuhan 430077, China)

Abstract

Analyzing electrical power generation for a wind turbine has associated inaccuracies due to fluctuations in environmental factors, mechanical alterations of wind turbines, and natural disaster. Thus, development of a highly reliable prediction model based on climatic conditions is crucial in forecasting electrical power for proper management of energy demand and supply. This is essential because early forecasting systems will enable an energy supplier to schedule and manage resources efficiently. In this research, we have put forward a novel electrical power prediction model using wavelet and particle swarm optimization based dual-stage adaptive neuro-fuzzy inference system (dual-stage Wavelet-PSO-ANFIS) for precise estimation of electrical power generation based on climatic factors. The first stage is used to project wind speed based on meteorological data available, while the second stage took the output wind speed prediction to predict electrical power based on actual supervisory control and data acquisition (SCADA). Furthermore, influence of data dependence on the forecasting accuracy for both stages is analyzed using a subset of data as input to predict the wind power which was also compared with other existing electrical power forecasting techniques. This paper defines the basic framework and the performance evaluation of a dual-stage Wavelet-PSO-ANFIS based electrical power forecasting system using a practical implementation.

Suggested Citation

  • Samuel Atuahene & Yukun Bao & Yao Yevenyo Ziggah & Patricia Semwaah Gyan & Feng Li, 2018. "Short-Term Electric Power Forecasting Using Dual-Stage Hierarchical Wavelet- Particle Swarm Optimization- Adaptive Neuro-Fuzzy Inference System PSO-ANFIS Approach Based On Climate Change," Energies, MDPI, vol. 11(10), pages 1-19, October.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:10:p:2822-:d:176820
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    References listed on IDEAS

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    Cited by:

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    5. Mohammad Ehteram & Ali Najah Ahmed & Chow Ming Fai & Haitham Abdulmohsin Afan & Ahmed El-Shafie, 2019. "Accuracy Enhancement for Zone Mapping of a Solar Radiation Forecasting Based Multi-Objective Model for Better Management of the Generation of Renewable Energy," Energies, MDPI, vol. 12(14), pages 1-26, July.

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