A Distributed PV System Capacity Estimation Approach Based on Support Vector Machine with Customer Net Load Curve Features

The stratospheric airship is a kind of aircraft that completely relies on the cycle of photovoltaic energy systems to achieve long duration flight. The accurate estimation of the operating temperature of solar cell modules on stratospheric airship is extremely important for the design of photovoltaics system (PV system), the output power calculation of PV system, and the calculation of energy balance. However, the related study has been rarely reported. A support vector machine prediction method based on particle swarm optimization algorithm (PSO-SVM) was established to predict the operating temperature of solar cell modules on stratospheric airship. The PSO algorithm was used to dynamically optimize the SVM’s parameters between the operating temperature of the solar cell modules and the measured data such as atmospheric pressure, solar radiation intensity, flight speed, and ambient temperature. The operating temperature data of the two sets of solar cell modules measured in the flight test were used to verify the accuracy of the temperature prediction model, and the prediction results were compared with a back propagation neural network (BPNN) method and the simulation results calculated by COMSOL Multiphysics of COMSOL, Inc., Columbus, MA, USA. The results shown that the PSO-SVM model realized the accurate prediction of the operating temperature of solar cell modules on stratospheric airship, which can guide the design of PV system, the output power calculation of PV system, and the calculation of energy balance.

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Research of MPPT Using Support Vector Machine for PV System

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.441.268 ◽

2013 ◽

Vol 441 ◽

pp. 268-271

Author(s):

De Da Sun ◽

Da Hai Zhang ◽

Yang Liu

Keyword(s):

Support Vector Machine ◽

Power Systems ◽

High Efficiency ◽

Environmental Changes ◽

Maximum Power ◽

Solar Irradiation ◽

Support Vector ◽

Pv System ◽

Maximum Power Point ◽

Power Point

Photovoltaic (PV) power systems are widely used today, so its useful to study how to make the PV maximum power output. In this paper a novel approach based on Support Vector Machine (SVM) for maximum power point tracking (MPPT) of PV systems is presented. The output power characteristics of PV cells vary with solar irradiation and temperature, so the controllers inputs is the level of solar radiation and ambient temperature of the PV module, and the voltage at maximum power point (MPP) is the output. Results show that the proposed MPPT controller based on SVM is sensitive to environmental changes and has high efficiency and less Mean Square Error (MSE).

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Commensurate Evaluation of Support Vector Machine and Recurrent Neural Network MPPT Algorithm for a PV system under different weather conditions

2019 11th International Conference on Electrical and Electronics Engineering (ELECO) ◽

10.23919/eleco47770.2019.8990468 ◽

2019 ◽

Author(s):

Mpho Sam Nkambule ◽

Ali N. Hasan ◽

Ahmed Ali

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Recurrent Neural Network ◽

Weather Conditions ◽

Support Vector ◽

Pv System

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Residential Energy Consumer Occupancy Prediction Based on Support Vector Machine

Sustainability ◽

10.3390/su13158321 ◽

2021 ◽

Vol 13 (15) ◽

pp. 8321

Author(s):

Dinh Hoa Nguyen

Keyword(s):

Support Vector Machine ◽

Energy Demand ◽

Electricity Consumption ◽

Support Vector ◽

Residential Energy ◽

Positive Definite Kernels ◽

Load Curve ◽

Performance Indexes ◽

Conditionally Positive Definite ◽

Occupancy Prediction

The occupancy of residential energy consumers is an important subject to be studied to account for the changes on the load curve shape caused by paradigm shifts to consumer-centric energy markets or by significant energy demand variations due to pandemics, such as COVID-19. For non-intrusive occupancy analysis, multiple types of sensors can be installed to collect data based on which the consumer occupancy can be learned. However, the overall system cost will be increased as a result. Therefore, this research proposes a cheap and lightweight machine learning approach to predict the energy consumer occupancy based solely on their electricity consumption data. The proposed approach employs a support vector machine (SVM), in which different kernels are used and compared, including positive semi-definite and conditionally positive definite kernels. Efficiency of the proposed approach is depicted by different performance indexes calculated on simulation results with a realistic, publicly available dataset. Among SVM models with different kernels, those with Gaussian (rbf) and sigmoid kernels have the highest performance indexes, hence they may be most suitable to be used for residential energy consumer occupancy prediction.

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Capacity estimation of large-scale retired li-ion batteries for second use based on support vector machine

2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE) ◽

10.1109/isie.2014.6864859 ◽

2014 ◽

Cited By ~ 3

Author(s):

Zheng Fangdan ◽

Jiang Jiuchun ◽

Zhang Weige ◽

Sun Bingxiang ◽

Zhang Caiping ◽

...

Keyword(s):

Support Vector Machine ◽

Large Scale ◽

Support Vector ◽

Li Ion Batteries ◽

Capacity Estimation ◽

Li Ion ◽

Second Use

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Prediction of solar irradiance using grey wolf Optimizer-Least-Square support vector machine

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v17.i1.pp10-17 ◽

2020 ◽

Vol 17 (1) ◽

pp. 10 ◽

Cited By ~ 1

Author(s):

Zuhaila Mat Yasin ◽

Nur Ashida Salim ◽

Nur Fadilah Ab Aziz ◽

Hasmaini Mohamad ◽

Norfishah Ab Wahab

Keyword(s):

Support Vector Machine ◽

Solar Irradiance ◽

Nonlinear Problems ◽

Least Square ◽

Main Step ◽

Support Vector ◽

Grey Wolf Optimizer ◽

Grey Wolf ◽

Particle Swarm Optimizer ◽

Pv System

<span>Prediction of solar irradiance is important for minimizing energy costs and providing high power quality in a photovoltaic (PV) system. This paper proposes a new technique for prediction of hourly-ahead solar irradiance namely Grey Wolf Optimizer- Least-Square Support Vector Machine (GWO-LSSVM). Least Squares Support Vector Machine (LSSVM) has strong ability to learn a complex nonlinear problems. In GWO-LSSVM, the parameters of LSSVM are optimized using Grey Wolf Optimizer (GWO). GWO algorithm is derived based on the hierarchy of leadership and the grey wolf hunting mechanism in nature. The main step of the grey wolf hunting mechanism are hunting, searching, encircling, and attacking the prey. The model has four input vectors: time, relative humidity, wind speed and ambient temperature. Mean Absolute Performance Error (MAPE) is used to measure the prediction performance. Comparative study also carried out using LSSVM and Particle Swarm Optimizer-Least Square Support Vector Machine (PSO-LSSVM). The results showed that GWO-LSSVM predicts more accurate than other techniques. </span>

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Temperature Prediction of Photovoltaic Panels Based on Support Vector Machine with Pigeon-Inspired Optimization

Complexity ◽

10.1155/2020/9278162 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Siyuan Fan ◽

Shengxian Cao ◽

Yanhui Zhang

Keyword(s):

Support Vector Machine ◽

Prediction Method ◽

Predictive Performance ◽

Support Vector ◽

Local Optimum ◽

Data Sets ◽

Pv System ◽

Temperature Prediction ◽

Output Stability ◽

Delay Factor

The output stability of the photovoltaic (PV) system is directly affected by temperature change of PV panels. In this paper, a novel temperature prediction method of PV panels with support vector machine (SVM) is proposed, which can solve the temperature prediction problem in a complex environment. In order to optimize parameters of SVM, a Pigeon-Inspired Optimization (PIO) method is given. Meanwhile, the delay factor (DF) is added to improve the PIO algorithm for avoiding the problem of local optimum. Moreover, a multisensor monitoring system of PV is established, and the collected data of temperature are used to train and verify the accuracy of the model. Finally, the proposed method is evaluated using synthetic and actual data sets. Simulation results show that the DFPIO-SVM can obtain better predictive performance.

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Sequent extended Kalman filter capacity estimation method for lithium-ion batteries based on discrete battery aging model and support vector machine

Journal of Energy Storage ◽

10.1016/j.est.2021.102594 ◽

2021 ◽

Vol 39 ◽

pp. 102594

Author(s):

Tao Sun ◽

Renjie Wu ◽

Yifan Cui ◽

Yuejiu Zheng

Keyword(s):

Support Vector Machine ◽

Kalman Filter ◽

Lithium Ion Batteries ◽

Extended Kalman Filter ◽

Estimation Method ◽

Lithium Ion ◽

Support Vector ◽

Capacity Estimation ◽

Aging Model

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PV Forecasting Using Support Vector Machine Learning in a Big Data Analytics Context

Symmetry ◽

10.3390/sym10120748 ◽

2018 ◽

Vol 10 (12) ◽

pp. 748 ◽

Cited By ~ 13

Author(s):

Stefan Preda ◽

Simona-Vasilica Oprea ◽

Adela Bâra ◽

Anda Belciu (Velicanu)

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Big Data ◽

Power Systems ◽

Management Strategies ◽

Big Data Analytics ◽

Support Vector ◽

Load Curve ◽

Big Data Technologies ◽

Accurate Forecast

Renewable energy systems (RES) are reliable by nature; the sun and wind are theoretically endless resources. From the beginnings of the power systems, the concern was to know “how much” energy will be generated. Initially, there were voltmeters and power meters; nowadays, there are much more advanced solar controllers, with small displays and built-in modules that handle big data. Usually, large photovoltaic (PV)-battery systems have sophisticated energy management strategies in order to operate unattended. By adding the information collected by sensors managed with powerful technologies such as big data and analytics, the system is able to efficiently react to environmental factors and respond to consumers’ requirements in real time. According to the weather parameters, the output of PV could be symmetric, supplying an asymmetric electricity demand. Thus, a smart adaptive switching module that includes a forecasting component is proposed to improve the symmetry between the PV output and daily load curve. A scaling approach for smaller off-grid systems that provides an accurate forecast of the PV output based on data collected from sensors is developed. The proposed methodology is based on sensor implementation in RES operation and big data technologies are considered for data processing and analytics. In this respect, we analyze data captured from loggers and forecast the PV output with Support Vector Machine (SVM) and linear regression, finding that Root Mean Square Error (RMSE) for prediction is considerably improved when using more parameters in the machine learning process.

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A Regional Day-Ahead Rooftop Photovoltaic Generation Forecasting Model Considering Unauthorized Photovoltaic Installation

Energies ◽

10.3390/en14144256 ◽

2021 ◽

Vol 14 (14) ◽

pp. 4256

Author(s):

Taeyoung Kim ◽

Jinho Kim

Keyword(s):

Power Systems ◽

Absolute Error ◽

Support Vector ◽

Capacity Estimation ◽

Pv Systems ◽

Estimation Capacity ◽

Load Pattern ◽

Pv Generation ◽

Net Load

Rooftop photovoltaic (PV) systems are usually behind the meter and invisible to utilities and retailers and, thus, their power generation is not monitored. If a number of rooftop PV systems are installed, it transforms the net load pattern in power systems. Moreover, not only generation but also PV capacity information is invisible due to unauthorized PV installations, causing inaccuracies in regional PV generation forecasting. This study proposes a regional rooftop PV generation forecasting methodology by adding unauthorized PV capacity estimation. PV capacity estimation consists of two steps: detection of unauthorized PV generation and estimation capacity of detected PV. Finally, regional rooftop PV generation is predicted by considering unauthorized PV capacity through the support vector regression (SVR) and upscaling method. The results from a case study show that compared with estimation without unauthorized PV capacity, the proposed methodology reduces the normalized root mean square error (nRMSE) by 5.41% and the normalized mean absolute error (nMAE) by 2.95%, It can be concluded that regional rooftop PV generation forecasting accuracy is improved.

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