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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Bibliographic Details
Published in:Journal of engineering (Stevenage, England) Vol. 2019; no. 18; pp. 4852 - 4857
Main Authors: Sujil, A, Kumar, Rajesh, Bansal, Ramesh C
Format: Journal Article
Language:English
Published: The Institution of Engineering and Technology 01.07.2019
Wiley
Subjects:
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
Online Access:Get full text
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Description
Summary: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