scholarly journals Model for Predicting the Operating Temperature of Stratospheric Airship Solar Cells with a Support Vector Machine

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1228
Author(s):  
Xuwei Wang ◽  
Zhaojie Li ◽  
Yanlei Zhang
Keyword(s):  
Support Vector Machine ◽  
Solar Cell ◽  
Energy Balance ◽  
Output Power ◽  
Operating Temperature ◽  
Support Vector ◽  
Power Calculation ◽  
Pv System ◽  
Stratospheric Airship ◽  
Cell Modules

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.

scholarly journals An Improved Whale Algorithm for Support Vector Machine Prediction of Photovoltaic Power Generation

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 212
Author(s):  
Yu-Wei Liu ◽  
Huan Feng ◽  
Heng-Yi Li ◽  
Ling-Ling Li
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Output Power ◽  
Input Data ◽  
Support Vector Machine Model ◽  
Weather Conditions ◽  
Clean Energy ◽  
Support Vector ◽  
Machine Model ◽  
Photovoltaic Power

Accurate prediction of photovoltaic power is conducive to the application of clean energy and sustainable development. An improved whale algorithm is proposed to optimize the Support Vector Machine model. The characteristic of the model is that it needs less training data to symmetrically adapt to the prediction conditions of different weather, and has high prediction accuracy in different weather conditions. This study aims to (1) select light intensity, ambient temperature and relative humidity, which are strictly related to photovoltaic output power as the input data; (2) apply wavelet soft threshold denoising to preprocess input data to reduce the noise contained in input data to symmetrically enhance the adaptability of the prediction model in different weather conditions; (3) improve the whale algorithm by using tent chaotic mapping, nonlinear disturbance and differential evolution algorithm; (4) apply the improved whale algorithm to optimize the Support Vector Machine model in order to improve the prediction accuracy of the prediction model. The experiment proves that the short-term prediction model of photovoltaic power based on symmetry concept achieves ideal accuracy in different weather. The systematic method for output power prediction of renewable energy is conductive to reducing the workload of predicting the output power and to promoting the application of clean energy and sustainable development.

Research of MPPT Using Support Vector Machine for PV System

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).

scholarly journals Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony Algorithm: A Case Study of Grid-Connected PV System Power Prediction

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Xiang-ming Gao ◽  
Shi-feng Yang ◽  
San-bo Pan
Keyword(s):  
Prediction Model ◽  
Output Power ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Time Series Data ◽  
Series Data ◽  
Support Vector ◽  
Power Prediction ◽  
Pv System ◽  
Bee Colony

Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD) and support vector machine (SVM) optimized with an artificial bee colony (ABC) algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization.

scholarly journals Solar Power Prediction via Support Vector Machine and Random Forest

2018 ◽  
Vol 69 ◽  
pp. 01004 ◽  
Author(s):  
Chih-Feng Yen ◽  
He-Yen Hsieh ◽  
Kuan-Wu Su ◽  
Min-Chieh Yu ◽  
Jenq-Shiou Leu
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Output Power ◽  
Environmental Parameters ◽  
Energy Market ◽  
Support Vector ◽  
Learning Models ◽  
Power Prediction ◽  
Machine Learning Models

Due to the variability and instability of photovoltaic (PV) output, the accurate prediction of PV output power plays a major role in energy market for PV operators to optimize their profits in energy market. In order to predict PV output, environmental parameters such as temperature, humidity, rainfall and win speed are gathered as indicators and different machine learning models are built for each solar panel inverters. In this paper, we propose two different kinds of solar prediction schemes for one-hour ahead forecasting of solar output using Support Vector Machine (SVM) and Random Forest (RF).

scholarly journals Concentrating System’s Design and Performance Analysis for Spacial Solar Array

2018 ◽  
Vol 36 (3) ◽  
pp. 471-479
Author(s):  
Limin Shao ◽  
Shuli Yang
Keyword(s):  
Solar Cells ◽  
Solar Cell ◽  
Output Power ◽  
Open Circuit Voltage ◽  
Operating Temperature ◽  
Short Circuit ◽  
Short Circuit Current ◽  
Open Circuit ◽  
Solar Array ◽  
Maximum Output

A large area of sunlight onto solar cells is gathered by concentrating system for spacial concentrating solar array, which reduces the amount of solar cells by increasing light intensity onto the solar cells of the unit area. Under concentrating conditions, the short-circuit current, open-circuit voltage, fill factor, efficiency, operating temperature and strong thermal-electrical coupling characteristics of concentrating solar cells are different from the conventional solar cells because of the high intensity and high operating temperature. The concentrating module design, solar cell selection, and design of solar cell heat-dissipation have been carried out. The thermal-electric coupling model of special concentrating photovoltaic system has been established. The relationships among concentrated ratio, substrate-thickness, thermal conductivity of substrate-material and solar cell’s temperature, density of short-circuit current, open-circuit voltage, maximum output power have been analyzed, which provide a view to a reasonabl0e match and selection of multi-parameters in engineering design. Results show that the concentrated ratio has an overall effect on the open-circuit voltage, short-circuit current, efficiency and operating temperature of the solar cell. There is a strong coupling relationship among the parameters, and the positive and negative impacts caused by the concentrating characteristics should be weighed in the engineering design. The short-circuit current density of concentrating solar cells is proportional to the concentrated ratio. Under the lower concentrated ratio circumstance, fill factor and efficiency is not substantially affected by the concentrated ratio. The maximum output power and open-circuit voltage increase with the increase of concentrated ratio. Temperature of concentrating solar cells has an adverse effect on the open-circuit voltage, efficiency and output power, which needs high efficient radiator measures to be taken. The operating temperature of solar cells could be decreased significantly by the high thermal conductivity of the substrate-material. The concentrated ratio between 9~15 is recommended for spacial solar array, which not only embodies the advantage of concentrator like improving the cell-efficiency and decreasing the cost, but also doesn’t exact the deploying precision of concentrating system.

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

2020 ◽  
Vol 17 (1) ◽  
pp. 10 ◽  
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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