Improved ANN Model for Predicting the AC Energy Output of a Realistic Photovoltaic Grid Connected PV System

2019 ◽  
Author(s):  
Sivasankari Sundaram
Keyword(s):  
Energy Output ◽  
Ann Model ◽  
Pv System

scholarly journals Techno-economic feasibility analysis of a 3-kW PV system installation in Nepal

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Ramhari Poudyal ◽  
Pavel Loskot ◽  
Ranjan Parajuli
Keyword(s):  
Net Present Value ◽  
Meteorological Data ◽  
Electric Energy ◽  
Simulation Software ◽  
Economic Feasibility ◽  
Performance Ratio ◽  
Energy Output ◽  
Solar Pv ◽  
Economic Returns ◽  
Pv System

AbstractThis study investigates the techno-economic feasibility of installing a 3-kilowatt-peak (kWp) photovoltaic (PV) system in Kathmandu, Nepal. The study also analyses the importance of scaling up the share of solar energy to contribute to the country's overall energy generation mix. The technical viability of the designed PV system is assessed using PVsyst and Meteonorm simulation software. The performance indicators adopted in our study are the electric energy output, performance ratio, and the economic returns including the levelised cost and the net present value of energy production. The key parameters used in simulations are site-specific meteorological data, solar irradiance, PV capacity factor, and the price of electricity. The achieved PV system efficiency and the performance ratio are 17% and 84%, respectively. The demand–supply gap has been estimated assuming the load profile of a typical household in Kathmandu under the enhanced use of electric appliances. Our results show that the 3-kWp PV system can generate 100% of electricity consumed by a typical residential household in Kathmandu. The calculated levelised cost of energy for the PV system considered is 0.06 $/kWh, and the corresponding rate of investment is 87%. The payback period is estimated to be 8.6 years. The installation of the designed solar PV system could save 10.33 tons of CO2 emission over its lifetime. Overall, the PV systems with 3 kWp capacity appear to be a viable solution to secure a sufficient amount of electricity for most households in Kathmandu city.

scholarly journals Forecasting and Modelling the Uncertainty of Low Voltage Network Demand and the Effect of Renewable Energy Sources

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2151
Author(s):  
Feras Alasali ◽  
Husam Foudeh ◽  
Esraa Mousa Ali ◽  
Khaled Nusair ◽  
William Holderbaum
Keyword(s):  
Renewable Energy ◽  
Low Voltage ◽  
Renewable Energy Sources ◽  
Golden Ratio ◽  
Clean Energy ◽  
Energy Sources ◽  
Percentage Error ◽  
Ann Model ◽  
Demand Forecast ◽  
Pv System

More and more households are using renewable energy sources, and this will continue as the world moves towards a clean energy future and new patterns in demands for electricity. This creates significant novel challenges for Distribution Network Operators (DNOs) such as volatile net demand behavior and predicting Low Voltage (LV) demand. There is a lack of understanding of modern LV networks’ demand and renewable energy sources behavior. This article starts with an investigation into the unique characteristics of householder demand behavior in Jordan, connected to Photovoltaics (PV) systems. Previous studies have focused mostly on forecasting LV level demand without considering renewable energy sources, disaggregation demand and the weather conditions at the LV level. In this study, we provide detailed LV demand analysis and a variety of forecasting methods in terms of a probabilistic, new optimization learning algorithm called the Golden Ratio Optimization Method (GROM) for an Artificial Neural Network (ANN) model for rolling and point forecasting. Short-term forecasting models have been designed and developed to generate future scenarios for different disaggregation demand levels from households, small cities, net demands and PV system output. The results show that the volatile behavior of LV networks connected to the PV system creates substantial forecasting challenges. The mean absolute percentage error (MAPE) for the ANN-GROM model improved by 41.2% for household demand forecast compared to the traditional ANN model.

scholarly journals Performance Evaluation of 100kWp Roof-top Grid-connected PV System installed in Northern India

2019 ◽  
Vol 8 (3) ◽  
pp. 8441-8444 ◽  
Keyword(s):  
Degradation Rate ◽  
Capacity Utilization ◽  
Plant Performance ◽  
Performance Ratio ◽  
Energy Output ◽  
System Efficiency ◽  
Pv System ◽  
Utilization Factor ◽  
Northern India ◽  
Efficiency Performance

The performance of 100 kWp roof-top grid-connected PV system was evaluated. The plant was installed at PGDM building in Sharda University, Greater Noida in northern India. The plant was monitored from March 2018 to February 2019. Performance parameters such as system efficiency, performance ratio, capacity utilization factor, and degradation rate were obtained. The plant performance result was compared with the estimated results obtained from SAM and PVsyst software. The total annual energy output was found to be 16426 kWh. The annual average system efficiency and capacity utilization factor of the plant was found to be 15.62 % and 14.72 % respectively. The annual performance ratio and annual degradation rate were found to be 76% and 1.28%/year respectively. The annual performance ratio obtained from SAM and PVsyst was found to be 78% and 82% respectively. It was noticed that the measured performance ratio was highly relative with the one obtained from SAM software.

scholarly journals Machine Learning Based Photovoltaics (PV) Power Prediction Using Different Environmental Parameters of Qatar

Energies ◽  
2019 ◽  
Vol 12 (14) ◽  
pp. 2782 ◽  
Author(s):  
Amith Khandakar ◽  
Muhammad E. H. Chowdhury ◽  
Monzure- Khoda Kazi ◽  
Kamel Benhmed ◽  
Farid Touati ◽  
...  
Keyword(s):  
Feature Selection ◽  
Output Power ◽  
Regression Models ◽  
Prediction Models ◽  
Gaussian Process Regression ◽  
Environmental Parameters ◽  
Ann Model ◽  
Power Prediction ◽  
Pv System ◽  
Energy Evaluation

Photovoltaics (PV) output power is highly sensitive to many environmental parameters and the power produced by the PV systems is significantly affected by the harsh environments. The annual PV power density of around 2000 kWh/m2 in the Arabian Peninsula is an exploitable wealth of energy source. These countries plan to increase the contribution of power from renewable energy (RE) over the years. Due to its abundance, the focus of RE is on solar energy. Evaluation and analysis of PV performance in terms of predicting the output PV power with less error demands investigation of the effects of relevant environmental parameters on its performance. In this paper, the authors have studied the effects of the relevant environmental parameters, such as irradiance, relative humidity, ambient temperature, wind speed, PV surface temperature and accumulated dust on the output power of the PV panel. Calibration of several sensors for an in-house built PV system was described. Several multiple regression models and artificial neural network (ANN)-based prediction models were trained and tested to forecast the hourly power output of the PV system. The ANN models with all the features and features selected using correlation feature selection (CFS) and relief feature selection (ReliefF) techniques were found to successfully predict PV output power with Root Mean Square Error (RMSE) of 2.1436, 6.1555, and 5.5351, respectively. Two different bias calculation techniques were used to evaluate the instances of biased prediction, which can be utilized to reduce bias to improve accuracy. The ANN model outperforms other regression models, such as a linear regression model, M5P decision tree and gaussian process regression (GPR) model. This will have a noteworthy contribution in scaling the PV deployment in countries like Qatar and increase the share of PV power in the national power production.

scholarly journals Investigation of Solar Photovoltaic-Thermal (PVT) and Solar Photovoltaic (PV) Performance: A Case Study in Ghana

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2701 ◽  
Author(s):  
Saeed Abdul-Ganiyu ◽  
David A Quansah ◽  
Emmanuel W Ramde ◽  
Razak Seidu ◽  
Muyiwa S. Adaramola
Keyword(s):  
Alternative Energy ◽  
Crystalline Silicon ◽  
Real Life ◽  
Electrical Energy ◽  
Energy Output ◽  
Total Output ◽  
Energy Yield ◽  
Solar Photovoltaic ◽  
Pv System ◽  
Yearly Average

The main objective of this paper is to experimentally assess the real-life outdoor performance of a photovoltaic-thermal (PVT) module against a conventional photovoltaic (PV) system in a hot humid tropical climate in Ghana. An experimental setup comprising a water-based mono-crystalline silicon PVT and an ordinary mono-crystalline silicon PV was installed on a rooftop at the Kwame Nkrumah University of Science and Technology in Kumasi and results evaluated for the entire year of 2019. It was observed that the annual total output energy of PV module was 194.79 kWh/m2 whereas that of the PVT for electrical and thermal outputs were 149.92 kWh/m2 and 1087.79 kWh/m2, respectively. The yearly average daily electrical energy yield for the PV and PVT were 3.21 kWh/kWp/day and 2.72 kWh/kWp/day, respectively. The annual performance ratios for the PV and PVT (based on electrical energy output only) were 79.2% and 51.6%, respectively, whilst their capacity factors were, respectively, 13.4% and 11.3%. Whereas the highest monthly mean efficiency recorded for the PV was 12.7%, the highest combined measured monthly mean electrical/thermal efficiency of the PVT was 56.1%. It is also concluded that the PVT is a worthy prospective alternative energy source in off-grid situations.

scholarly journals Short-Term Forecasting of Electric Energy Generation for a Photovoltaic System

2018 ◽  
Vol 155 ◽  
pp. 01033 ◽  
Author(s):  
V.T. Dinh ◽  
Yuhao Yan
Keyword(s):  
Electric Energy ◽  
Photovoltaic System ◽  
Energy Output ◽  
Non Destructive Testing ◽  
Short Term ◽  
Destructive Testing ◽  
Pv System ◽  
Short Term Forecasting ◽  
Non Destructive ◽  
Good Agreement

This article presents a short-term forecast of electric energy output of a photovoltaic (PV) system towards Tomsk city, Russia climate variations (module temperature and solar irradiance). The system is located at Institute of Non-destructive Testing, Tomsk Polytechnic University. The obtained results show good agreement between actual data and prediction values.

scholarly journals A Monitoring System for Online Fault Detection and Classification in Photovoltaic Plants

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4688 ◽  
Author(s):  
André Eugênio Lazzaretti ◽  
Clayton Hilgemberg da Costa ◽  
Marcelo Paludetto Rodrigues ◽  
Guilherme Dan Yamada ◽  
Gilberto Lexinoski ◽  
...  
Keyword(s):  
Fault Detection ◽  
Monitoring System ◽  
Environmental Variables ◽  
Energy Use ◽  
Short Circuit ◽  
Environmental Parameters ◽  
Open Circuit ◽  
Ann Model ◽  
Pv System ◽  
Fault Detection And Classification

Photovoltaic (PV) energy use has been increasing recently, mainly due to new policies all over the world to reduce the application of fossil fuels. PV system efficiency is highly dependent on environmental variables, besides being affected by several kinds of faults, which can lead to a severe energy loss throughout the operation of the system. In this sense, we present a Monitoring System (MS) to measure the electrical and environmental variables to produce instantaneous and historical data, allowing to estimate parameters that ar related to the plant efficiency. Additionally, using the same MS, we propose a recursive linear model to detect faults in the system, while using irradiance and temperature on the PV panel as input signals and power as output. The accuracy of the fault detection for a 5 kW power plant used in the test is 93.09%, considering 16 days and around 143 hours of faults in different conditions. Once a fault is detected by this model, a machine-learning-based method classifies each fault in the following cases: short-circuit, open-circuit, partial shadowing, and degradation. Using the same days and faults applied in the detection module, the accuracy of the classification stage is 95.44% for an Artificial Neural Network (ANN) model. By combining detection and classification, the overall accuracy is 92.64%. Such a result represents an original contribution of this work, since other related works do not present the integration of a fault detection and classification approach with an embedded PV plant monitoring system, allowing for the online identification and classification of different PV faults, besides real-time and historical monitoring of electrical and environmental parameters of the plant.

scholarly journals A Meta Study on the Implications of Thermoelectric Generation on Hybrid Photovoltaic Systems

2018 ◽  
Vol 5 ◽  
pp. 104-118
Author(s):  
Jamison Ghinis ◽  
Clifford Leslie
Keyword(s):  
Hybrid Systems ◽  
Considerable Effect ◽  
Operating Conditions ◽  
Energy Output ◽  
Maximum Efficiency ◽  
Practical Implementation ◽  
Total Efficiency ◽  
Maximum Output ◽  
Pv System ◽  
Specific Temperature

The focus of this paper is a meta-study analysis of the efficiency of hybrid thermal and photovoltaic (PV) energy systems and how various materials and specific temperature ranges for thermoelectric (TE) generation can increase their efficiency. This meta-study focuses on papers obtained from ACS NANO, Scopus, Web of Science and Nature which discuss the theoretical and practical implementation of TE and PV systems, with various hybrid systems being considered. Analysed is the Figure of Merit from various hybrid TE and PV integrated systems, the effect of energy efficiency and power generation on different PV system temperatures, and output over area. The total efficiency of the hybrid system is found to have a considerable effect in all papers analysed, with an increase of 5 to 10 percent efficiency in energy output due to the thermoelectric generator (TEG) section, with this maximum efficiency occurring approximately in a 25 kelvin range [1]. A maximum output of 125 W peaks can be maintained for systems efficiently over 600 W/m2 modules, this is an up to 5 percent total efficiency increase in power output in the previously discussed 25 kelvin range [2]. The papers proposed demonstrate the more efficient implementations, potential for further study and implementation of hybrid systems within specific temperature and operating conditions.

scholarly journals A Critical Appraisal of PV-Systems’ Performance

Buildings ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 192
Author(s):  
Zainab Usman ◽  
Joseph Tah ◽  
Henry Abanda ◽  
Charles Nche
Keyword(s):  
Renewable Energy ◽  
Electric Energy ◽  
Geographical Location ◽  
Poor Performance ◽  
Performance Ratio ◽  
Energy Output ◽  
Pv System ◽  
Pv Systems ◽  
Energy Technologies ◽  
And Performance

Climate change and global warming have triggered a global increase in the use of renewable energy for various purposes. In recent years, the photovoltaic (PV)-system has become one of the most popular renewable energy technologies that captures solar energy for different applications. Despite its popularity, its adoption is still facing enormous challenges, especially in developing countries. Experience from research and practice has revealed that installed PV-systems significantly underperform. This has been one of the major barriers to PV-system adoption, yet it has received very little attention. The poor performance of installed PV-systems means they do not generate the required electric energy output they have been designed to produce. Performance assessment parameters such as performance yields and performance ratio (PR) help to provide mathematical accounts of the expected energy output of PV-systems. Many reasons have been advanced for the disparity in the performance of PV-systems. This study aims to analyze the factors that affect the performance of installed PV-systems, such as geographical location, solar irradiance, dust, and shading. Other factors such as multiplicity of PV-system components in the market and the complexity of the permutations of these components, their types, efficiencies, and their different performance indicators are poorly understood, thus making it difficult to optimize the efficiency of the system as a whole. Furthermore, mathematical computations are presented to prove that the different design methods often used for the design of PV-systems lead to results with significant differences due to different assumptions often made early on. The methods for the design of PV-systems are critically appraised. There is a paucity of literature about the different methods of designing PV-systems, their disparities, and the outcomes of each method. The rationale behind this review is to analyze the variations in designs and offer far-reaching recommendations for future studies so that researchers can come up with more standardized design approaches.

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