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

Sujil, A.; Kumar, Rajesh; Bansal, Ramesh C.

doi:10.1049/joe.2018.9323

Cited by 39 publications

(18 citation statements)

References 33 publications

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“…), mean squared error (MSE), and root mean squared error (RMSE). The formulations of the considered statical criteria are shown in (10)(11)(12)(13). These criteria are most commonly used to report the accuracy of forecasting in the literature [31][32][33].…”

Section:  mentioning

confidence: 99%

“…In [10], the Artificial Neural Networks (ANNs) were used to forecast the PV unit generation for the PMGs' day-ahead operation. Sujil et al [11] presented an adaptive neuro-fuzzy inference system (ANFIS)-based PV and WT generation forecasting model for the PMGs' EMS.…”

Section: Introductionmentioning

confidence: 99%

“… Proposing a novel optimal operation of PMGs using simultaneous forecasting of demand and supply side's parameters; Although several papers in the literature, such as [9][10][11][22][23][24][25] [19,20]. However, they considered a "pre-defined" load pattern without any particular load forecasting methodology.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Day-Ahead Self-Scheduling and Operation of Prosumer Microgrids Using Hybrid Machine Learning-Based Weather and Load Forecasting

et al. 2020

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Prosumer microgrids (PMGs) are considered as active users in smart grids. These units are able to generate and sell electricity to aggregators or neighbor consumers in the prosumer market. Although the optimal scheduling and operation of PMGs have received a great deal of attention in recent studies, the challenges of PMG's uncertainties such as stochastic behavior of load data and weather conditions (solar irradiance, ambient temperature, and wind speed) and corresponding solutions have not been thoroughly investigated. In this paper, a new energy management systems (EMS) based on weather and load forecasting is proposed for PMG's optimal scheduling and operation. Developing a novel hybrid machine learning-based method using adaptive neuro-fuzzy inference system (ANFIS), multilayer perceptron (MLP) artificial neural network (ANN), and radial basis function (RBF) ANN to precisely predict the load and weather data is one of the most important contributions of this article. The performance of the forecasting process is improved by using a hybrid machine learning-based forecasting method instead of conventional ones. The demand response (DR) program based on the forecasted data and considering the degradation cost of the battery storage system (BSS) are other contributions. The comparison of obtained test results with those of other existing approaches illustrates that more appropriate PMG's operation cost is achievable by applying the proposed DR-based EMS using a new hybrid machine learning forecasting method.

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Section:  mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal Day-Ahead Self-Scheduling and Operation of Prosumer Microgrids Using Hybrid Machine Learning-Based Weather and Load Forecasting

et al. 2020

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“…Xu [18] proposed a concise zero-order Sugeno-Takagi (TSK) inference system based on enhanced soft subspace clustering (ESSC) and sparse leading (SL) to improve the clarity and interpretability of fuzzy reasoning systems. Sujil [19] proposed wind power generation prediction agents for multiple-agent-based energy management systems in smart microgrids using subtraction clustering and fuzzy clustering methods. A fuzzy-based hyper-round strategy (FHRP) was introduced by Neamatollani [20] to plan clustering operations easily and flexibly.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neuro-Fuzzy Inference System Predictor with an Incremental Tree Structure Based on a Context-Based Fuzzy Clustering Approach

Yeom

Kwak

2020

Applied Sciences

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We propose an adaptive neuro-fuzzy inference system (ANFIS) with an incremental tree structure based on a context-based fuzzy C-means (CFCM) clustering process. ANFIS is a combination of a neural network with the ability to learn, adapt and compute, and a fuzzy machine with the ability to think and to reason. It has the advantages of both models. General ANFIS rule generation methods include a method employing a grid division using a membership function and a clustering method. In this study, a rule is created using CFCM clustering that considers the pattern of the output space. In addition, multiple ANFISs were designed in an incremental tree structure without using a single ANFIS. To evaluate the performance of ANFIS in an incremental tree structure based on the CFCM clustering method, a computer performance prediction experiment was conducted using a building heating-and-cooling dataset. The prediction experiment verified that the proposed CFCM-clustering-based ANFIS shows better prediction efficiency than the current grid-based and clustering-based ANFISs in the form of an incremental tree.

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“…It provides the necessary information for future solar irradiance forecasting approaches for efficient management of grid. A study related to overcurrent management and protection in a solar PV‐based DG power system has been given in Vander Walt et al The adaptive neuro fuzzy inference system (ANFIS) to forecast the wind and PV power has been presented in Sujil et al for energy management in the context of the smart grid.…”

Section: Introductionmentioning

confidence: 99%

Frequency regulation in islanded microgrid considering stochastic model of wind and PV

Kumar

Mathur

Bhanot

et al. 2019

Int Trans Electr Energ Syst

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Summary This paper addresses the method of forecasting the wind and solar power and its application to an islanded microgrid (MG) model for load frequency control. Due to high penetration of renewable energy sources, the islanded MG suffers from lower equivalent inertia. The islanded MG faces several challenges in order to ensure the stable operation by maintaining the frequency and voltage at nominal value. The supply and demand power mismatch is mainly due to continuously changing solar irradiance, fluctuating wind speed, variable inertia, and load fluctuations. The intermittent nature of RESs can significantly affect the system stability; hence, the challenge lies in accurate forecasting of power from the renewable energy sources (RESs) so that a proactive arrangement is made available for compensation of active power or frequency variations. The forecasting will determine the correct estimate of power availability so that the power reserves can be activated prior to large variations in active power affecting the stability of the MGs. To address these challenges, a stochastic model of wind and solar has been developed using “Time series modeling” of the data obtained from Charanka Solar Park under Gujarat Energy Development Agency, India. Wind and solar power availability are forecasted using autoregressive integrated moving average (ARIMA) method including the seasonality factor. The proportional and integral (PI) controller is used for regulating the frequency fluctuations caused due to intermittency in the output of RESs and load power. Various load patterns are applied to the MG model to analyze its load frequency behavior along with variations in secondary sources.

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

Cited by 39 publications

References 33 publications

Optimal Day-Ahead Self-Scheduling and Operation of Prosumer Microgrids Using Hybrid Machine Learning-Based Weather and Load Forecasting

Optimal Day-Ahead Self-Scheduling and Operation of Prosumer Microgrids Using Hybrid Machine Learning-Based Weather and Load Forecasting

Adaptive Neuro-Fuzzy Inference System Predictor with an Incremental Tree Structure Based on a Context-Based Fuzzy Clustering Approach

Frequency regulation in islanded microgrid considering stochastic model of wind and PV

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