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

The Journal of Engineering

  ^{1    2    3} 

[1] Department of Electrical & Computer Engineering, College of Engineering, University of Sharjah, Sharjah, UAE;Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, India;Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, India;School of Electrical Skills, Bhartiya Skill Development University, Jaipur, India;

关键词: fuzzy logic;    optimisation;    inference mechanisms;    distributed power generation;    load forecasting;    fuzzy reasoning;    neural nets;    pattern clustering;    energy management systems;    adaptive systems;    fuzzy set theory;    photovoltaic power systems;    wind power plants;    power engineering computing;    learning (artificial intelligence);    multi-agent systems;    smart microgrid;    wind output power generation forecasting agent;    multiagent-based energy management system;    microgrid EMS;    load forecast;    effective dispatch strategies;    forecasting accuracy;    adaptive neuro-fuzzy inference system;    PV generation forecasting agent;    microgrid energy management;    fuzzy logic inference;    PV historical data;    historical wind data;    hybrid optimisation algorithm-based ANFIS model;    FCM Clustering-ANFIS;    neural network;    linguistic expression function;    fuzzy c means clustering;   

DOI  :  10.1049/joe.2018.9323

来源: publisher

【 摘 要 】

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.

【 授权许可】

CC BY