scholarly journals Hour-Ahead Photovoltaic Output Forecasting Using Wavelet-ANFIS

Mathematics ◽  
2021 ◽  
Vol 9 (19) ◽  
pp. 2438
Author(s):  
Chao-Rong Chen ◽  
Faouzi Brice Ouedraogo ◽  
Yu-Ming Chang ◽  
Devita Ayu Larasati ◽  
Shih-Wei Tan
Keyword(s):  
Output Power ◽  
Power Output ◽  
Wavelet Transforms ◽  
Fuzzy Inference ◽  
Model Performance ◽  
Integrated System ◽  
Discrete Wavelet ◽  
Short Term ◽  
Correct Operation ◽  
Inference System

The operational challenge of a photovoltaic (PV) integrated system is the uncertainty (irregularity) of the future power output. The integration and correct operation can be carried out with accurate forecasting of the PV output power. A distinct artificial intelligence method was employed in the present study to forecast the PV output power and investigate the accuracy using endogenous data. Discrete wavelet transforms were used to decompose PV output power into approximate and detailed components. The decomposed PV output was fed into an adaptive neuro-fuzzy inference system (ANFIS) input model to forecast the short-term PV power output. Various wavelet mother functions were also investigated, including Haar, Daubechies, Coiflets, and Symlets. The proposed model performance was highly correlated to the input set and wavelet mother function. The statistical performance of the wavelet-ANFIS was found to have better efficiency compared with the ANFIS and ANN models. In addition, wavelet-ANFIS coif2 and sym4 offer the best precision among all the studied models. The result highlights that the combination of wavelet decomposition and the ANFIS model can be a helpful tool for accurate short-term PV output forecasting and yield better efficiency and performance than the conventional model.

scholarly journals Hybrid Forecasting Model for Short-Term Electricity Market Prices with Renewable Integration

2018 ◽  
Vol 11 (1) ◽  
pp. 57 ◽  
Author(s):  
Gerardo Osório ◽  
Mohamed Lotfi ◽  
Miadreza Shafie-khah ◽  
Vasco Campos ◽  
João Catalão
Keyword(s):  
Electricity Market ◽  
Wavelet Transforms ◽  
Fuzzy Inference ◽  
Electricity Sector ◽  
Forecasting Model ◽  
Probabilistic Forecasting ◽  
Short Term ◽  
Market Prices ◽  
Inference System ◽  
Hybrid Particle Swarm Optimization

In recent years, there have been notable commitments and obligations by the electricity sector for more sustainable generation and delivery processes to reduce the environmental footprint. However, there is still a long way to go to achieve necessary sustainability goals while ensuring standards of robustness and the quality of power grids. One of the main challenges hindering this progress are uncertainties and stochasticity associated with the electricity sector and especially renewable generation. In this paradigm shift, forecasting tools are indispensable, and their utilization can significantly improve system operation and minimize costs associated with all related activities. Thus, forecasting tools have an essential key role in all decision-making stages. In this work, a hybrid probabilistic forecasting model (HPFM) was developed for short-term electricity market prices (EMP) combining wavelet transforms (WT), hybrid particle swarm optimization (DEEPSO), adaptive neuro-fuzzy inference system (ANFIS), and Monte Carlo simulation (MCS). The proposed hybrid probabilistic forecasting model (HPFM) was tested and validated with real data from the Spanish and Pennsylvania-New Jersey-Maryland (PJM) markets. The proposed model exhibited favorable results and performance in comparison with previously published work considering electricity market prices (EMP) data, which is notable.

scholarly journals Simulation study and experimental results for detection and classification of the transient capacitor inrush current using discrete wavelet transform and artificial intelligence

Open Physics ◽  
2018 ◽  
Vol 16 (1) ◽  
pp. 93-104 ◽  
Author(s):  
Theerasak Patcharoen ◽  
Suntiti Yoomak ◽  
Atthapol Ngaopitakkul ◽  
Chaichan Pothisarn
Keyword(s):  
Artificial Intelligence ◽  
Simulation Study ◽  
Wavelet Transforms ◽  
Fuzzy Inference ◽  
Capacitor Bank ◽  
Experimental Results ◽  
Discrete Wavelet ◽  
Inrush Current ◽  
Discrete Wavelet Transforms ◽  
Inference System

Abstract This paper describes the combination of discrete wavelet transforms (DWT) and artificial intelligence (AI), which are efficient techniques to identify the type of inrush current, analyze the origin and possible cause on the capacitor bank switching. The experiment setup used to verify the proposed techniques can be detected and classified the transient inrush current from normal capacitor rated current. The discrete wavelet transforms are used to detect and classify the inrush current. Then, output from wavelet is acted as input of fuzzy inference system for discriminating the type of switching transient inrush current. The proposed technique shows enhanced performance with a discrimination accuracy of 90.57%. Both simulation study and experimental results are quite satisfactory with providing the high accuracy and reliability which can be developed and implemented into a numerical overcurrent (50/51) and unbalanced current (60C) protection relay for an application of shunt capacitor bank protection in the future.

Study on the Output Power of the PV Power Plant Model Based on ANFIS

2013 ◽  
Vol 724-725 ◽  
pp. 190-194
Author(s):  
Hai Feng Liang ◽  
Hai Hong Wang ◽  
Zi Xing Liu
Keyword(s):  
Power Plant ◽  
Output Power ◽  
Power Output ◽  
Fuzzy Inference ◽  
Engineering Application ◽  
Subtractive Clustering ◽  
Interval Estimate ◽  
Pv System ◽  
Inference System ◽  
Output Characteristics

in order to study the output power of PV plant in depth, effective and reasonable methods of modeling for PV power plant are explored and adaptive neuro-fuzzy inference system (ANFIS) based on Takagi-Sugeno (TS) model is proposed in this paper. According to the power output characteristics of PV system and a variety of factors which impact, three kinds of model of PV plant power output are established based on subtractive clustering ANFIS. After model test and calculation for confidence interval estimate of power output, the results show that the accuracy of the model is able to meet the practical engineering application requirements and the second model is optimal by comparison. In conclusion, ANFIS provides an innovative and feasible model establishment method for the power output of PV plant.

scholarly journals IDENTIFYING KEY FACTORS OF COMMUTER TRAIN SERVICE QUALITY:AN EMPIRICAL ANALYSIS FOR DHAKA CITY

2019 ◽  
Vol 31 (2) ◽  
Author(s):  
Anika Nowshin Mowrin ◽  
Md. Hadiuzzaman ◽  
Saurav Barua ◽  
Md. Mizanur Rahman
Keyword(s):  
Traffic Congestion ◽  
Fuzzy Inference ◽  
Confusion Matrix ◽  
Model Development ◽  
Model Performance ◽  
Total Sample ◽  
Dhaka City ◽  
Anfis Model ◽  
Regular User ◽  
Inference System

Commuter train is a viable alternative to road transport to ease the traffic congestion which requires appropriate planning by concerned authorities. The research is aimed to assess passengers’ perception about commuter train service running in areas near Dhaka city. An Adaptive Neuro Fuzzy Inference System (ANFIS) model has been developed to evaluate service quality (SQ) of commuter train. Field survey data has been conducted among 802 respondents who were the regular user of commuter train and 12 attributes have been selected for model development. ANFIS was developed by the training and then tested by 80% and 20% of the total sample respectively. After that, model performance has been evaluated by (i) Confusion Matrix (ii) Root Mean Square Error (RMSE) and attributes are ranked based on their relative importance. The proposed ANFIS model has 61.50% accuracy in training and 47.80% accuracy in testing.  From the results, it is found that 'Bogie condition', 'Cleanliness', ‘Female harassment’, 'Behavior of staff' and 'Toilet facility' are the most significant attributes. This indicates that some necessary measures should be taken immediately to recover the effects of these attributes to improve the SQ of commuter train. 

Smoothing the output power of a wind energy conversion system using a hybrid nonlinear pitch angle controller

2021 ◽  
pp. 014459872110417
Author(s):  
Ya-Jun Fan ◽  
Hai-tong Xu ◽  
Zhao-Yu He
Keyword(s):  
Fuzzy Logic ◽  
Wind Energy ◽  
Output Power ◽  
Wind Turbines ◽  
Fuzzy Inference System ◽  
Pitch Angle ◽  
Fuzzy Inference ◽  
Hybrid Control ◽  
Inference System ◽  
Neuro Fuzzy

Wind energy has been developed and is widely used as a clean and renewable form of energy. Among the existing variety of wind turbines, variable-speed variable-pitch wind turbines have become popular owing to their variable output power capability. In this study, a hybrid control strategy is proposed to implement pitch angle control. A new nonlinear hybrid control approach based on the Adaptive Neuro-Fuzzy Inference System and fuzzy logic control is proposed to regulate the pitch angle and maintain the captured mechanical energy at the rated value. In the controller, the reference value of the pitch angle is predicted by the Adaptive Neuro-Fuzzy Inference System according to the wind speed and the blade tip speed ratio. A proposed fuzzy logic controller provides feedback based on the captured power to modify the pitch angle in real time. The effectiveness of the proposed hybrid pitch angle control method was verified on a 5 MW offshore wind turbine under two different wind conditions using MATLAB/Simulink. The simulation results showed that fluctuations in rotor speed were dramatically mitigated, and the captured mechanical power was always near the rated value as compared with the performance when using the Adaptive Neuro-Fuzzy Inference System alone. The variation rate of power was 0.18% when the proposed controller was employed, whereas it was 2.93% when only an Adaptive Neuro-Fuzzy Inference System was used.

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