FCM Clustering-ANFIS-based PV and wind generation forecasting agent for energy management in a smart microgrid

This paper proposes a PV and wind output power generation forecasting agent for a multi-agent-based energy management system (EMS) in a smart microgrid. The microgrid EMS requires both generation forecast and load forecast to provide effective dispatch strategies. The efficiency of the EMS significa...

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Vydáno v:	Journal of engineering (Stevenage, England) Ročník 2019; číslo 18; s. 4852 - 4857
Hlavní autoři:	Sujil, A, Kumar, Rajesh, Bansal, Ramesh C
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	The Institution of Engineering and Technology 01.07.2019 Wiley
Témata:	adaptive neuro-fuzzy inference system adaptive systems distributed power generation effective dispatch strategies energy management systems FCM Clustering‐ANFIS forecasting accuracy fuzzy c means clustering fuzzy logic fuzzy logic inference fuzzy reasoning fuzzy set theory historical wind data hybrid optimisation algorithm-based anfis model inference mechanisms learning (artificial intelligence) linguistic expression function load forecast load forecasting microgrid EMS microgrid energy management multi-agent systems multiagent-based energy management system neural nets neural network optimisation pattern clustering photovoltaic power systems power engineering computing PV generation forecasting agent PV historical data smart microgrid The 7th International Conference on Renewable Power Generation (RPG 2018) wind output power generation forecasting agent wind power plants microgrid EMS multi-agent systems fuzzy set theory distributed power generation fuzzy reasoning neural network historical wind data optimisation PV generation forecasting agent learning (artificial intelligence) multiagent-based energy management system wind power plants FCM Clustering-ANFIS smart microgrid forecasting accuracy microgrid energy management fuzzy logic photovoltaic power systems inference mechanisms effective dispatch strategies power engineering computing adaptive systems load forecast PV historical data fuzzy logic inference linguistic expression function energy management systems pattern clustering load forecasting adaptive neuro-fuzzy inference system wind output power generation forecasting agent neural nets fuzzy c means clustering hybrid optimisation algorithm-based ANFIS model
ISSN:	2051-3305, 2051-3305
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Popis
Shrnutí:	This paper proposes a PV and wind output power generation forecasting agent for a multi-agent-based energy management system (EMS) in a smart microgrid. The microgrid EMS requires both generation forecast and load forecast to provide effective dispatch strategies. The efficiency of the EMS significantly relies on its forecasting accuracy. Firstly, this paper develops an adaptive neuro-fuzzy inference system (ANFIS)-based forecasting model and then utilise it for the development of wind and PV generation forecasting agent for microgrid energy management. ANFIS adopt the self-learning capability from the neural network and linguistic expression function from fuzzy logic inference and stands at the top of both the technologies in performance. The proposed model has been tested using two data sets, i.e., PV historical data and historical wind data. The fuzzy c means clustering (FCM) with hybrid optimisation algorithm-based ANFIS model shows better forecasting accuracy with both PV and wind forecast, therefore, implemented as PV and wind forecasting agent for microgrid EMS.
ISSN:	2051-3305 2051-3305
DOI:	10.1049/joe.2018.9323