scholarly journals Comparative Study of Photovoltaic Power Forecasting Methods

2020 ◽  
Author(s):  
Angelo Pelisson ◽  
Thiago Covoes ◽  
Anderson Spengler ◽  
Pablo Jaskowiak
Keyword(s):  
Power Generation ◽  
Electricity Consumption ◽  
Renewable Energy Resources ◽  
Short Term ◽  
Operational Conditions ◽  
Regression Methods ◽  
Pv Systems ◽  
Production Variability ◽  
Energy Grid ◽  
Pv Power Generation

Electricity consumption is growing rapidly worldwide. Renewable energy resources, such as solar energy, play a crucial role in this scenario, contributing to satisfy demand sustainability. Although the share of Photovoltaic (PV) power generation has increased in the past years, PV systems are quite sensitive to climatic and meteorological conditions, leading to undesirable power production variability. In order to improve energy grid stability, reliability, and management, accurate forecasting models that relate operational conditions to power output are needed. In this work we evaluate the performance of regression methods applied to forecast short term (next day) energy production of a PV Plant. Specifically, we consider five regression methods and different configurations of feature sets. Our results suggest that MLP and SVR provide the best forecasting results, in general. Also, although features based on different solar irradiance levels play a key role in predicting power generation, the use of additional features can improve prediction results.

scholarly journals Ultra-Short-Term Forecasting of Photo-Voltaic Power via RBF Neural Network

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1717
Author(s):  
Wanxing Ma ◽  
Zhimin Chen ◽  
Qing Zhu
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Smart Grids ◽  
Percentage Error ◽  
Solar Pv ◽  
Short Term ◽  
Forecasting Method ◽  
Photo Voltaic ◽  
Short Term Forecasting ◽  
Pv Power Generation

With the fast expansion of renewable energy systems during recent years, the stability and quality of smart grids using solar energy have been challenged because of the intermittency and fluctuations. Hence, forecasting photo-voltaic (PV) power generation is essential in facilitating planning and managing electricity generation and distribution. In this paper, the ultra-short-term forecasting method for solar PV power generation is investigated. Subsequently, we proposed a radial basis function (RBF)-based neural network. Additionally, to improve the network generalization ability and reduce the training time, the numbers of hidden layer neurons are limited. The input of neural network is selected as the one with higher Spearman correlation among the predicted power features. The data are normalized and the expansion parameter of RBF neurons are adjusted continuously in order to reduce the calculation errors and improve the forecasting accuracy. Numerous simulations are carried out to evaluate the performance of the proposed forecasting method. The mean absolute percentage error (MAPE) of the testing set is within 10%, which show that the power values of the following 15 min. can be predicted accurately. The simulation results verify that our method shows better performance than other existing works.

scholarly journals Nanostructures GaAs Solar Cells

2020 ◽  
Author(s):  
Mohammad Nur E Alam ◽  
Narottam Das
Keyword(s):  
Power Generation ◽  
Solar Cells ◽  
Conversion Efficiency ◽  
Human Mobility ◽  
Incident Light ◽  
Front Surface ◽  
Pv Systems ◽  
Energy Demands ◽  
Power Generation Technology ◽  
Pv Power Generation

At present, the world is now passing a very far different time than normal situation due to the COVID-19 pandemic crisis. The global life-style and human civilization is currently progressing with down-stream that affecting almost every sectors necessary for human civilizations except the current environmental situation. To control the COVID-19 spreading, most of the countries are following lockdown process that reduces human mobility, thus reducing the CO2 emission to the environment. Though the COVID-19 pandemic is a blessing for the present environment, however, the post-COVID world will face a massive thrust of energy and only conventional energy resources may not be enough to mitigate the energy demands. Solar power generation technology mainly the photovoltaic (PV) systems and their advancement can be the leading possibilities to minimize the gap between the power demand and generation. It is now time to think how we can improve the PV power generation in future and the post-COVID world. In this encyclopaedia communication, we report on Nano-technological approach to improve the conversion efficiency of GaAs solar cells. We have designed and optimized several types of nano-structured assemblies that can be implemented to reduce the front surface incident light reflection losses thus can assist to improve the conversion efficiency of GaAs solar cells.

scholarly journals Algorithm Study on Improving Application Efficiency of Independent Solar Power Supply System

2016 ◽  
Vol 3 (1) ◽  
pp. 5
Author(s):  
Jigang Cao
Keyword(s):  
Power Generation ◽  
Maximum Power Point Tracking ◽  
Maximum Power ◽  
Maximum Power Point ◽  
Pv Systems ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Important Means ◽  
Power Point ◽  
Pv Power Generation

<p class="p1"><span class="s1">With the development of photovoltaic (PV) technologies, applications of photovoltaic have grown rapidly, indicating that the photovoltaic are attractive to produce environmentally benign electricity for diversified purposes. In order to maximize the use of solar energy, this thesis focuses on the PV power generation systems, which includes modeling of PV systems, maximum power point tracking (MPPT) methods for PV arrays. </span><span class="s1">Maximum Power Point Tracking (MPPT) method is an important means to improve the system efficiency of PV power generation system. MPPT theory and various MPPT algorithms are introduced in the literature. Based on those researches, this thesis proposes a novel implementation of an adaptive duty cycle P&amp;O algorithm that can reduce the main drawbacks commonly related to the traditional P&amp;O method.</span></p>

scholarly journals Forecasting Day-Ahead Hourly Photovoltaic Power Generation Using Convolutional Self-Attention Based Long Short-Term Memory

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 4017 ◽  
Author(s):  
Dukhwan Yu ◽  
Wonik Choi ◽  
Myoungsoo Kim ◽  
Ling Liu
Keyword(s):  
Power Generation ◽  
Deep Learning ◽  
Prediction Accuracy ◽  
Short Term Memory ◽  
Local Context ◽  
Short Term ◽  
Term Memory ◽  
Proposed Model ◽  
Long Short Term Memory ◽  
Pv Power Generation

The problem of Photovoltaic (PV) power generation forecasting is becoming crucial as the penetration level of Distributed Energy Resources (DERs) increases in microgrids and Virtual Power Plants (VPPs). In order to improve the stability of power systems, a fair amount of research has been proposed for increasing prediction performance in practical environments through statistical, machine learning, deep learning, and hybrid approaches. Despite these efforts, the problem of forecasting PV power generation remains to be challenging in power system operations since existing methods show limited accuracy and thus are not sufficiently practical enough to be widely deployed. Many existing methods using long historical data suffer from the long-term dependency problem and are not able to produce high prediction accuracy due to their failure to fully utilize all features of long sequence inputs. To address this problem, we propose a deep learning-based PV power generation forecasting model called Convolutional Self-Attention based Long Short-Term Memory (LSTM). By using the convolutional self-attention mechanism, we can significantly improve prediction accuracy by capturing the local context of the data and generating keys and queries that fit the local context. To validate the applicability of the proposed model, we conduct extensive experiments on both PV power generation forecasting using a real world dataset and power consumption forecasting. The experimental results of power generation forecasting using the real world datasets show that the MAPEs of the proposed model are much lower, in fact by 7.7%, 6%, 3.9% compared to the Deep Neural Network (DNN), LSTM and LSTM with the canonical self-attention, respectively. As for power consumption forecasting, the proposed model exhibits 32%, 17% and 44% lower Mean Absolute Percentage Error (MAPE) than the DNN, LSTM and LSTM with the canonical self-attention, respectively.

scholarly journals A Novel Ultra-Short-Term PV Power Forecasting Method Based on DBN-Based Takagi-Sugeno Fuzzy Model

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6447
Author(s):  
Ling Liu ◽  
Fang Liu ◽  
Yuling Zheng
Keyword(s):  
Power Generation ◽  
Fuzzy Model ◽  
Redundant Information ◽  
Short Term ◽  
Forecasting Method ◽  
Input Variables ◽  
Takagi Sugeno ◽  
Pv Power Generation ◽  
Fuzzy Subsets ◽  
Power Forecasting

Forecasting uncertainties limit the development of photovoltaic (PV) power generation. New forecasting technologies are urgently needed to improve the accuracy of power generation forecasting. In this paper, a novel ultra-short-term PV power forecasting method is proposed based on a deep belief network (DBN)-based Takagi-Sugeno (T-S) fuzzy model. Firstly, the correlation analysis is used to filter redundant information. Furthermore, a T-S fuzzy model, which integrates fuzzy c-means (FCM) for the fuzzy division of input variables and DBN for fuzzy subsets forecasting, is developed. Finally, the proposed method is compared to a benchmark DBN method and the T-S fuzzy model in case studies. The numerical results show the feasibility and flexibility of the proposed ultra-short-term PV power forecasting approach.

Simulation About Suitable Direction of Photovoltaic Array for Residential Solar Power Generation System

Solar Energy ◽  
2003 ◽  
Author(s):  
Koji Sakai ◽  
Osamu Ishihara ◽  
Akio Tanaka ◽  
Hidetoshi Nakagami ◽  
Masaki Manabe
Keyword(s):  
Power Generation ◽  
Tilt Angle ◽  
Generation System ◽  
Pv Systems ◽  
Photovoltaic Array ◽  
Solar Power Generation ◽  
Installation Angle ◽  
Power Generation System ◽  
Pv Power Generation ◽  
Fixed System

In this paper, it is attempted in the present study to estimate the factors to reduce power generating efficiency using the results of measurements and to calculate the optimal tilt angle in Kyushu area. Also, the study was made as to how the annual power generation amount is influenced by the deviation of the optimal tilt angle from actual installation angle. We have obtained the results that modeling was performed on the factors to reduce the efficiency obtained from measurements, and PV power generation amount in Kyushu area was estimated. Also, from the results of calculation of the optimal installation angle of the fixed system at installation site, it was demonstrated that the installation of PV systems on roof surface as generally practiced at present did not lead to a greater loss.

scholarly journals Nanostructures GaAs solar cells

2020 ◽  
Author(s):  
Mohammad Nur E Alam ◽  
Narottam Das
Keyword(s):  
Power Generation ◽  
Solar Cells ◽  
Conversion Efficiency ◽  
Human Mobility ◽  
Incident Light ◽  
Front Surface ◽  
Pv Systems ◽  
Energy Demands ◽  
Power Generation Technology ◽  
Pv Power Generation

At present, the world is now passing a very far different time than normal situation due to the COVID-19 pandemic crisis. The global life-style and human civilization is currently progressing with down-stream that affecting almost every sectors necessary for human civilizations except the current environmental situation. To control the COVID-19 spreading, most of the countries are following lockdown process that reduces human mobility, thus reducing the CO2 emission to the environment. Though the COVID-19 pandemic is a blessing for the present environment, however, the post-COVID world will face a massive thrust of energy and only conventional energy resources may not be enough to mitigate the energy demands. Solar power generation technology mainly the photovoltaic (PV) systems and their advancement can be the leading possibilities to minimize the gap between the power demand and generation. It is now time to think how we can improve the PV power generation in future and the post-COVID world. In this encyclopaedia communication, we report on Nano-technological approach to improve the conversion efficiency of GaAs solar cells. We have designed and optimized several types of nano-structured assemblies that can be implemented to reduce the front surface incident light reflection losses thus can assist to improve the conversion efficiency of GaAs solar cells.

Charging-Discharging Control with MPPT for Photovoltaic System

2011 ◽  
Vol 55-57 ◽  
pp. 1253-1257
Author(s):  
Xin Gao
Keyword(s):  
Power Generation ◽  
Control Strategy ◽  
Block Diagram ◽  
Photovoltaic System ◽  
Electric Heater ◽  
Renewable Energy Resources ◽  
Generation System ◽  
Control Block ◽  
Power Generation System ◽  
Pv Power Generation

Solar energy is the most viable renewable energy resources in the world. This paper presents a battery charging-discharging control strategy with MPPT for photovoltaic (PV) power generation system. If the PV power generation sources produce more energy than the requirement the loads, the surplus energy can be used either to charge the battery or to provide a dump load (electric heater or electrolysis-hydrogen). If the amount of energy demanded by the loads is higher than the production of the PV power generation sources, the control strategy determines the battery will release energy to cover the load requirements until the battery is fully discharged. This paper explains the strategy developed and shows the control block diagram of photovoltaic power generation system.

An Optimized MPP Tracking Algorithm under Partially Shaded Conditions in Photovoltaic System

2013 ◽  
Vol 860-863 ◽  
pp. 201-209
Author(s):  
Qing Zhong ◽  
Nan Hua Yu ◽  
Kun Wang ◽  
Lin Feng ◽  
Guo Jie Li ◽  
...  
Keyword(s):  
Power Generation ◽  
Tracking Algorithm ◽  
Photovoltaic System ◽  
The Real ◽  
Pv Systems ◽  
Pv Array ◽  
Simulation And Experiment ◽  
Improved Performance ◽  
Power Generation Systems ◽  
Pv Power Generation

In photovoltaic (PV) power generation systems, the P-V curve of PV array contains several peaks under partially shaded condition (PSC) so that conventional MPP tracking (MPPT) methods can hardly find the real MPP (MPP). This paper proposes an optimized MPPT strategy, which is not only fit for PSC but also suitable for uniform shaded conditions (USC). Comparing the optimized MPPT strategies and conventional MPPT methods, this paper illustrates the improved performance of optimized strategy under PSC. The simulation and experiment results show that the optimized strategy can track the global MPP precisely to improve the efficiency of PV systems.

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