Prediction of Photovoltaic Power Output Considering Weather Conditions

Solar Energy ◽  
2006 ◽  
Author(s):  
Kaoru Furushima ◽  
Yutaka Nawata ◽  
Michio Sadatomi
Keyword(s):  
Heat Transfer ◽  
Silicon Solar Cell ◽  
Weather Conditions ◽  
Weather Data ◽  
Cell Temperature ◽  
Pv System ◽  
Voltage Current Characteristic ◽  
Current Characteristics ◽  
Modified Equation ◽  
Electrical Output

A reasonable construction of photovoltaic (PV) system would be possible if the electrical output from the PV can be predicted accurately from weather data before the construction. The electrical output can be calculated theoretically from the voltage-current characteristics equation for a silicon solar cell, if the solar irradiance and the cell temperature are known. However, it is not easy to predict the cell temperature because it depends on several physical and environment factors; in particular, it is difficult to estimate a heat transfer from PV surface to surrounds. In this study, firstly, we confirmed that the electrical output can be predicted accurately by a modified equation of traditional voltage-current characteristic equation when the irradiance and the cell temperature are given. Secondly, we estimated the average heat transfer coefficient from PV surface as a function of wind speed using the experimental data on the PV system obtained in this study and compared it with three kinds of data in references. As a result, the estimated values agree with the references’ results in their accuracy of the data.

scholarly journals A Novel Hybrid Model for Short-Term Forecasting in PV Power Generation

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Yuan-Kang Wu ◽  
Chao-Rong Chen ◽  
Hasimah Abdul Rahman
Keyword(s):  
Power Output ◽  
Solar Power ◽  
Peak Load ◽  
Weather Conditions ◽  
Green Energy ◽  
Weather Data ◽  
Stable Operation ◽  
Short Term ◽  
Pv System ◽  
Experimental Database

The increasing use of solar power as a source of electricity has led to increased interest in forecasting its power output over short-time horizons. Short-term forecasts are needed for operational planning, switching sources, programming backup, reserve usage, and peak load matching. However, the output of a photovoltaic (PV) system is influenced by irradiation, cloud cover, and other weather conditions. These factors make it difficult to conduct short-term PV output forecasting. In this paper, an experimental database of solar power output, solar irradiance, air, and module temperature data has been utilized. It includes data from the Green Energy Office Building in Malaysia, the Taichung Thermal Plant of Taipower, and National Penghu University. Based on the historical PV power and weather data provided in the experiment, all factors that influence photovoltaic-generated energy are discussed. Moreover, five types of forecasting modules were developed and utilized to predict the one-hour-ahead PV output. They include the ARIMA, SVM, ANN, ANFIS, and the combination models using GA algorithm. Forecasting results show the high precision and efficiency of this combination model. Therefore, the proposed model is suitable for ensuring the stable operation of a photovoltaic generation system.

scholarly journals An Efficient Intelligent Power Detection Method for Photovoltaic System

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Detection Method ◽  
Peak Load ◽  
Weather Conditions ◽  
Photovoltaic System ◽  
Weather Data ◽  
Weather Factors ◽  
Pv System ◽  
Intelligent Technique ◽  
Highly Nonlinear ◽  
Validation Set

Jordan has experienced a significant increase in both peak load and annual electricity demand within the last decade due to the growth of the economy and population. Photovoltaic (PV) system is one of the most popular renewable energy source in Jordan. PV system is highly nonlinear with unpredictable behavior since it is always subject to many external factors such as severe weather conditions, irradiance level, sheds, temperature, etc. This makes it difficult to maintain maximum power production around its operation ranges. In this paper, an intelligent technique is used to predict and identify the working ability of the PV system under different weather factors in Tafila Technical University (TTU) in Jordan. It helps in optimizing power productions for different operation points. The PV system in Tafila with size 1 MWp PV generated 5.4 GWh since 2017. It saves about € 1.5 million in three years. A real power data from the PV system and a weather data from world weather online site of TTU location are used in this study. Decision tree technique is employed to identify the relation between the output power and weather factors. The results show that the system accuracy is 82.01% during the training phase and 93.425 % on the validation set.

scholarly journals PVcheck—A Software to Check Your Photovoltaic System

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6757
Author(s):  
Markus Rinio
Keyword(s):  
Peak Power ◽  
Weather Conditions ◽  
Data File ◽  
Photovoltaic System ◽  
Weather Data ◽  
Energy Yield ◽  
Pv System ◽  
Time Operation ◽  
Long Time ◽  
Module Efficiency

Having a photovoltaic (PV) system raises the question of whether it runs as expected. Measuring its energy yield takes a long time and the result still contains uncertainties from varying weather conditions and possible shading of the modules. Here, a free software PVcheck to measure the peak power of the system is announced, using the power data of a single sunny day. The software loads a data file of the generated power as a function of time from this day. This data file is provided by typical inverters. The software then simulates this power curve using known parameters like angle and location of the PV system. The assumed peak power of the simulation can then be adjusted so that the simulated curve matches the measured one. The software runs under Microsoft Windows™ and makes use of the free library pvlib python. The simulation can be refined by importing weather data like temperature, wind speed, and insolation. Furthermore, curves describing the nominal module efficiency as a function of the illumination intensity as well as the power-dependent inverter efficiency can be included in the simulation. First results reveal a good agreement of the simulation with experimental data. The software can be used to detect strong problems in PV systems after installation and to monitor their long-time operation.

scholarly journals Estimating Solar Insolation and Power Generation of Photovoltaic Systems Using Previous Day Weather Data

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Min Hee Chung
Keyword(s):  
Energy Production ◽  
Mean Squared Error ◽  
Weather Conditions ◽  
Absolute Error ◽  
Weather Data ◽  
Mean Bias Error ◽  
Bias Error ◽  
Feed Forward Neural Network ◽  
Pv System ◽  
Solar Insolation

Day-ahead predictions of solar insolation are useful for forecasting the energy production of photovoltaic (PV) systems attached to buildings, and accurate forecasts are essential for operational efficiency and trading markets. In this study, a multilayer feed-forward neural network-based model that predicts the next day’s solar insolation by taking into consideration the weather conditions of the present day was proposed. The proposed insolation model was employed to estimate the energy production of a real PV system located in South Korea. Validation research was performed by comparing the model’s estimated energy production with the measured energy production data collected during the PV system operation. The accuracy indices for the optimal model, which included the root mean squared error, mean bias error, and mean absolute error, were 1.43 kWh/m2/day, −0.09 kWh/m2/day, and 1.15 kWh/m2/day, respectively. These values indicate that the proposed model is capable of producing reasonable insolation predictions; however, additional work is needed to achieve accurate estimates for energy trading.

scholarly journals Performance Analysis and Comparison of a Concentrated Photovoltaic System with Different Phase Change Materials

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2911
Author(s):  
Jawad Sarwar ◽  
Muhammad Rizwan Shad ◽  
Arshmah Hasnain ◽  
Farman Ali ◽  
Konstantinos E. Kakosimos ◽  
...  
Keyword(s):  
Phase Change ◽  
Phase Change Materials ◽  
Crystalline Silicon ◽  
Weather Conditions ◽  
Photovoltaic System ◽  
Pv System ◽  
System A ◽  
Change Material ◽  
Fully Coupled ◽  
Electrical Output

In this work, temperature regulation and electrical output of a concentrated photovoltaic system coupled with a phase change material (CPVPCM) system is investigated and compared with a single sun crystalline photovoltaic (PV) system. A fully coupled thermal-optical-electrical model has been developed in-house to conduct the simulation studies for actual weather conditions of Doha (Qatar) and selected phase change materials (PCMs). The selected PCMs are lauric acid, RT47, S-series salt, STL47, ClimSelTM C48, RT54, RT60, RT62, and RT64. An optical concentration ratio of 20× is considered on a 15 mm wide crystalline silicon cell. The temperature evolution, thermal energy storage and electrical output of the CPVPCM system are obtained for 48-hour simulations with representative weather conditions for each month of a typical meteorological year (TMY). Results and overall thermal and electrical efficiency are compared for each PCM. In brief, the CPVPCM system with S-series salt performs better than all other PCM with an overall efficiency of 54.4%. Furthermore, this system consistently produces more power than a PV system with an equal footprint (1 m2) for each month of the TMY.

scholarly journals Performance Evaluation of Neural Network-Based Short-Term Solar Irradiation Forecasts

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3030
Author(s):  
Simon Liebermann ◽  
Jung-Sup Um ◽  
YoungSeok Hwang ◽  
Stephan Schlüter
Keyword(s):  
Neural Network ◽  
Goodness Of Fit ◽  
Renewable Energy Sources ◽  
Weather Conditions ◽  
Solar Irradiation ◽  
Weather Data ◽  
Short Term ◽  
Climate Conditions ◽  
Lstm Network ◽  
The Time Domain

Due to the globally increasing share of renewable energy sources like wind and solar power, precise forecasts for weather data are becoming more and more important. To compute such forecasts numerous authors apply neural networks (NN), whereby models became ever more complex recently. Using solar irradiation as an example, we verify if this additional complexity is required in terms of forecasting precision. Different NN models, namely the long-short term (LSTM) neural network, a convolutional neural network (CNN), and combinations of both are benchmarked against each other. The naive forecast is included as a baseline. Various locations across Europe are tested to analyze the models’ performance under different climate conditions. Forecasts up to 24 h in advance are generated and compared using different goodness of fit (GoF) measures. Besides, errors are analyzed in the time domain. As expected, the error of all models increases with rising forecasting horizon. Over all test stations it shows that combining an LSTM network with a CNN yields the best performance. However, regarding the chosen GoF measures, differences to the alternative approaches are fairly small. The hybrid model’s advantage lies not in the improved GoF but in its versatility: contrary to an LSTM or a CNN, it produces good results under all tested weather conditions.

scholarly journals Advanced Flight Planning and the Benefit of In-Flight Aircraft Trajectory Optimization

2021 ◽  
Vol 13 (3) ◽  
pp. 1383
Author(s):  
Judith Rosenow ◽  
Martin Lindner ◽  
Joachim Scheiderer
Keyword(s):  
New York ◽  
Traffic Control ◽  
Traffic Management ◽  
Trajectory Optimization ◽  
Service Providers ◽  
Weather Conditions ◽  
Air Traffic ◽  
Weather Data ◽  
Weather Forecasts ◽  
Aircraft Trajectory

The implementation of Trajectory-Based Operations, invented by the Single European Sky Air Traffic Management Research program SESAR, enables airlines to fly along optimized waypoint-less trajectories and accordingly to significantly increase the sustainability of the air transport system in a business with increasing environmental awareness. However, unsteady weather conditions and uncertain weather forecasts might induce the necessity to re-optimize the trajectory during the flight. By considering a re-optimization of the trajectory during the flight they further support air traffic control towards achieving precise air traffic flow management and, in consequence, an increase in airspace and airport capacity. However, the re-optimization leads to an increase in the operator and controller’s task loads which must be balanced with the benefit of the re-optimization. From this follows that operators need a decision support under which circumstances and how often a trajectory re-optimization should be carried out. Local numerical weather service providers issue hourly weather forecasts for the coming hour. Such weather data sets covering three months were used to re-optimize a daily A320 flight from Seattle to New York every hour and to calculate the effects of this re-optimization on fuel consumption and deviation from the filed path. Therefore, a simulation-based trajectory optimization tool was used. Fuel savings between 0.5% and 7% per flight were achieved despite minor differences in wind speed between two consecutive weather forecasts in the order of 0.5 m s−1. The calculated lateral deviations from the filed path within 1 nautical mile were always very small. Thus, the method could be easily implemented in current flight operations. The developed performance indicators could help operators to evaluate the re-optimization and to initiate its activation as a new flight plan accordingly.

A new IR and sprinkler based embedded controller directed robotic arm for automatic cleaning of solar panel

2019 ◽  
Vol 18 (4) ◽  
pp. 905-921
Author(s):  
Manish Kumar Ghodki ◽  
Akhilesh Swarup ◽  
Yash Pal
Keyword(s):  
Pulse Width Modulation ◽  
Weather Conditions ◽  
Solar Panel ◽  
Performance Ratio ◽  
Robotic Arm ◽  
Pv System ◽  
Content Type ◽  
Average Value ◽  
Ir Sensor ◽  
Embedded Controller

Purpose The purpose of this paper is to design and develop an IR and sprinkler based embedded controller operated robotic arm for automatic dust removal system to mitigate the dust effect on the solar panel surface, since dust accumulation normally affected by real weather conditions is one of the serious concern for the deterioration of photovoltaic (PV) system output. Design/methodology/approach The system is a wet cleaning device which provides a cheap silicon rubber-based wiping operation controlled by the pulse width modulation-operated motors of robotic arm. The IEEE 1149.1-compliant mixed signal-embedded platform of C8051F226DK is involved to command the complete system. Findings A prototype of 30 WP system is capable of producing an inspiring average value of 11.26 per cent in energy increase, 13.63 per cent in PV module efficiency and 85.20 per cent in performance ratio of the system after 73 days of cleaning in summer season. In addition, a total of 1,617.93 W power; 1,0516.55 Wh energy; and 350.55 KWh/KWP final yield was found during the entire cleaning period. Originality/value A novel technique of the implementation of IR sensor and sprinkler in dust mitigation is proposed in this paper. The IR sensor is used as a versatile object which can manage the robotic arm setting and control the automatic switching between cleaning and charging, as well as identify the thermal condition of solar panel for overheating.

Effect of Unglazed Transpired Collector on the Performance of a Polycrystalline Silicon Photovoltaic Module

2006 ◽  
Vol 128 (3) ◽  
pp. 349-353 ◽  
Author(s):  
A. T. Naveed ◽  
E. C. Kang ◽  
E. J. Lee
Keyword(s):  
Polycrystalline Silicon ◽  
Electrical Power ◽  
Heating System ◽  
Photovoltaic Module ◽  
Forced Ventilation ◽  
Pv System ◽  
Pv Module ◽  
Pv Modules ◽  
Typical Day ◽  
Electrical Output

The electrical power generated by a polycrystalline silicon photovoltaic (PV) module mounted on an unglazed transpired solar collector (UTC) has been studied and compared to that of a PV module without UTC for a quantitative analysis of electrical output and its role in reducing the simple payback periods of photovoltaic electrical systems. A 75W polycrystalline silicon PV module was fixed on an UTC in front of the ventilation fan, and effectiveness of cooling by means of the forced ventilation at the rate of 160CFM was monitored. The temperature reduction under forced ventilation was in the range of 3-9°C with a 5% recovery in the electrical output power on a typical day of the month of February 2005. The simulated and measured electrical power outputs are in reasonable agreement with root-mean-square error of 2.40. The life cycle assessment of a hypothetical PV system located at Daejeon, South Korea and consisting of 3kW PV modules fixed on a 50m2 UTC shows that with a possible reduction of 3-9°C in the operating temperatures, the system requires three 75W fewer PV modules. The simple payback period of PV system is reduced from 23yearsto15years when integrated into an UTC air heating system.

Cooling of Concentrator Photovoltaic Cells Using Mini-Scale Jet Impingement Heat Sinks

2018 ◽  
Author(s):  
Ali Radwan ◽  
Meshack Hawi ◽  
Mahmoud Ahmed
Keyword(s):  
Heat Transfer ◽  
Flow Model ◽  
Three Dimensional ◽  
Jet Impingement ◽  
Heat Sinks ◽  
Maximum Temperature ◽  
Transfer Model ◽  
Cell Temperature ◽  
Maximum Cell ◽  
T System

In this study, an efficient cooling technique for concentrator photovoltaic (CPV) cells is proposed to enhance the system electrical efficiency and extend its lifetime. To do this, a comprehensive three-dimensional conjugate heat transfer model of CPV cells layers coupled with the heat transfer and fluid flow model inside jet impingement heat sink is developed. Four different jet impingement designs are compared. The investigated designs are (A) central inlet jet, (B) Hypotenuse inlet jet, (C) staggered inlet jet, and (D) conventional jet impingement design with side drainage. The effect of coolant flowrate on the CPV/T system performance is investigated. The model is numerically simulated and validated using the available experiments. The performance of CPV system is investigated at solar concentration ratios of 20 and coolant flowrate up to 6000g/min. It is found that increasing the flowrate from 60 g/min to 600 g/min decrease the maximum cell temperature by 31°C for the configuration D while increasing the flowrate from 600 g/min to 6000 g/min reduce the cell temperature by 20.2°C. It is also concluded that at a higher flowrate of 6000g/min, all the investigated configurations relatively achieve better temperature uniformity with maximum temperature differences of 0.9 °C, 2.1 °C, 3.6 °C, and 3.9 °C for configurations A, B, C, and D respectively.

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