Hybrid neurofuzzy wind power forecast and wind turbine location for embedded generation

Paul A. Adedeji, Stephen A. Akinlabi, Nkosinathi Madushele, Obafemi O. Olatunji

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)

Abstract

Wind energy uptake in South Africa is significantly increasing both at the micro- and macro-level and the possibility of embedded generation cannot be undermined considering the state of electricity supply in the country. This study identifies a wind hotspot site in the Eastern Cape province, performs an in silico deployment of three utility-scale wind turbines of 60 m hub height each from different manufacturers, develops machine learning models to forecast very short-term power production of the three wind turbine generators (WTG) and investigates the feasibility of embedded generation for a potential livestock industry in the area. Windographer software was used to characterize and simulate the net output power from these turbines using the wind speed of the potential site. Two hybrid models of adaptive neurofuzzy inference system (ANFIS) comprising genetic algorithm and particle swarm optimization (PSO) each for a turbine were developed to forecast very short-term power output. The feasibility of embedded generation for typical medium-scale agricultural industry was investigated using a weighted Weber facility location model. The analytical hierarchical process (AHP) was used for weight determination. From our findings, the WTG-1 was selected based on its error performance metrics (root mean square error of 0.180, mean absolute SD of 0.091 and coefficient of determination of 0.914 and CT = 702.3 seconds) in the optimal model (PSO-ANFIS). Criteria were ranked based on their order of significance to the agricultural industry as proximity to water supply, labour availability, power supply and road network. Also, as a proof of concept, the optimal location of the industrial facility relative to other criteria was X = 19.24 m, Y = 47.11 m. This study reveals the significance of resource forecasting and feasibility of embedded generation, thus improving the quality of preliminary resource assessment and facility location among site developers.

Original languageEnglish
Pages (from-to)413-428
Number of pages16
JournalInternational Journal of Energy Research
Volume45
Issue number1
DOIs
Publication statusPublished - Jan 2021

Keywords

  • ANFIS
  • South Africa
  • embedded generation
  • genetic algorithm
  • particle swarm optimization
  • single facility location
  • utility-scale wind turbine
  • wind energy

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Nuclear Energy and Engineering
  • Fuel Technology
  • Energy Engineering and Power Technology

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