scholarly journals Advanced PV Performance Modelling Based on Different Levels of Irradiance Data Accuracy

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2166 ◽  
Author(s):  
Julián Ascencio-Vásquez ◽  
Jakob Bevc ◽  
Kristjan Reba ◽  
Kristijan Brecl ◽  
Marko Jankovec ◽  
...  
Keyword(s):  
Machine Learning ◽  
Weather Conditions ◽  
Empirical Models ◽  
Operating Conditions ◽  
Data Accuracy ◽  
Temperate Climate ◽  
Energy Yield ◽  
Pv Systems ◽  
Light Gradient ◽  
Irradiance Data

In photovoltaic (PV) systems, energy yield is one of the essential pieces of information to the stakeholders (grid operators, maintenance operators, financial units, etc.). The amount of energy produced by a photovoltaic system in a specific time period depends on the weather conditions, including snow and dust, the actual PV modules’ and inverters’ efficiency and balance-of-system losses. The energy yield can be estimated by using empirical models with accurate input data. However, most of the PV systems do not include on-site high-class measurement devices for irradiance and other weather conditions. For this reason, the use of reanalysis-based or satellite-based data is currently of significant interest in the PV community and combining the data with decomposition and transposition irradiance models, the actual Plane-of-Array operating conditions can be determined. In this paper, we are proposing an efficient and accurate approach for PV output energy modelling by combining a new data filtering procedure and fast machine learning algorithm Light Gradient Boosting Machine (LightGBM). The applicability of the procedure is presented on three levels of irradiance data accuracy (low, medium, and high) depending on the source or modelling used. A new filtering algorithm is proposed to exclude erroneous data due to system failures or unreal weather conditions (i.e., shading, partial snow coverage, reflections, soiling deposition, etc.). The cleaned data is then used to train three empirical models and three machine learning approaches, where we emphasize the advantages of the LightGBM. The experiments are carried out on a 17 kW roof-top PV system installed in Ljubljana, Slovenia, in a temperate climate zone.

scholarly journals Evaluation of Contribution of PV Array and Inverter Configurations to Rooftop PV System Energy Yield Using Machine Learning Techniques

Energies ◽  
2019 ◽  
Vol 12 (16) ◽  
pp. 3158 ◽  
Author(s):  
Ngoc Thien Le ◽  
Watit Benjapolakul
Keyword(s):  
Machine Learning ◽  
Investment Decision ◽  
High Volume ◽  
Machine Learning Techniques ◽  
Energy Yield ◽  
Pv System ◽  
Useful Knowledge ◽  
Pv Systems ◽  
Learning Techniques ◽  
The Difference

Rooftop photovoltaics (PV) systems are attracting residential customers due to their renewable energy contribution to houses and to green cities. However, customers also need a comprehensive understanding of system design configuration and the related energy return from the system in order to support their PV investment. In this study, the rooftop PV systems from many high-volume installed PV systems countries and regions were collected to evaluate the lifetime energy yield of these systems based on machine learning techniques. Then, we obtained an association between the lifetime energy yield and technical configuration details of PV such as rated solar panel power, number of panels, rated inverter power, and number of inverters. Our findings reveal that the variability of PV lifetime energy is partly explained by the difference in PV system configuration. Indeed, our machine learning model can explain approximately 31 % ( 95 % confidence interval: 29–38%) of the variant energy efficiency of the PV system, given the configuration and components of the PV system. Our study has contributed useful knowledge to support the planning and design of a rooftop PV system such as PV financial modeling and PV investment decision.

Study of Photovoltaic Systems’ Performances with Different Module Types

2016 ◽  
Vol 856 ◽  
pp. 279-284 ◽  
Author(s):  
Zahari Zarkov ◽  
Ludmil Stoyanov ◽  
Hristiyan Kanchev ◽  
Valentin Milenov ◽  
Vladimir Lazarov
Keyword(s):  
Theoretical Study ◽  
Weather Conditions ◽  
Operating Conditions ◽  
Experimental Results ◽  
Photovoltaic Systems ◽  
Experimental Site ◽  
Pv Systems ◽  
Research Platform ◽  
Pv Modules ◽  
Different Types

The purpose of the work is to study and compare the performance of photovoltaic (PV) generators built with different types of panels and operating in real weather conditions. The paper reports the results from an experimental and theoretical study of systems with PV modules manufactured according to different technologies and using different materials. The experiment was carried out at a research platform for PV systems developed by the authors, built and located at an experimental site near the Technical University of Sofia. Based on the obtained results, comparisons are made between the different PV generators for the same operating conditions. The comparison between the theoretical and the experimental results demonstrates a good level of overlap.

Performance of Bi-Facial PV Modules in Urban Settlements in Tropical Regions

2013 ◽  
Vol 853 ◽  
pp. 312-316
Author(s):  
Carlo Pisigan ◽  
Fan Jiang
Keyword(s):  
Urban Areas ◽  
Weather Conditions ◽  
Energy Output ◽  
Rear Side ◽  
Energy Yield ◽  
Tropical Regions ◽  
Pv Systems ◽  
Irradiation Energy ◽  
Pv Modules ◽  
Urban Settlements

This paper studies the performance of bifacial Heterojunction with Intrinsic Thin-layer (HIT) PV modules through a one-year experiment in Singapore. Two 1.2kWp (front side)/0.84kWp (rear side) PV systems were installed vertically, facing the N-S and E-W directions respectively. The operational data of two systems were monitored and collected to analyze their performance under different weather conditions. This paper will presentthe change of irradiation, energy yield and the AC energy output of the bifacial PV systems. The results help to understand the impacts of system installation on the energy yield of vertically-installedbifacial HIT PV systems, to find out its advantages in applications over monofacial PV modules and to explore the potential of bifacial PV modules in tropical regions, especially in urban areas like Singapore.

scholarly journals Machine Learning-Based Condition Monitoring for PV Systems: State of the Art and Future Prospects

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6316
Author(s):  
Tarek Berghout ◽  
Mohamed Benbouzid ◽  
Toufik Bentrcia ◽  
Xiandong Ma ◽  
Siniša Djurović ◽  
...  
Keyword(s):  
Machine Learning ◽  
Electric Power ◽  
Condition Monitoring ◽  
Failure Modes ◽  
Generative Models ◽  
Operating Conditions ◽  
Detection Methods ◽  
Photovoltaic Systems ◽  
Pv Systems ◽  
Maintenance Program

To ensure the continuity of electric power generation for photovoltaic systems, condition monitoring frameworks are subject to major enhancements. The continuous uniform delivery of electric power depends entirely on a well-designed condition maintenance program. A just-in-time task to deal with several naturally occurring faults can be correctly undertaken via the cooperation of effective detection, diagnosis, and prognostic analyses. Therefore, the present review first outlines different failure modes to which all photovoltaic systems are subjected, in addition to the essential integrated detection methods and technologies. Then, data-driven paradigms, and their contribution to solving this prediction problem, are also explored. Accordingly, this review primarily investigates the different learning architectures used (i.e., ordinary, hybrid, and ensemble) in relation to their learning frameworks (i.e., traditional and deep learning). It also discusses the extension of machine learning to knowledge-driven approaches, including generative models such as adversarial networks and transfer learning. Finally, this review provides insights into different works to highlight various operating conditions and different numbers and types of failures, and provides links to some publicly available datasets in the field. The clear organization of the abundant information on this subject may result in rigorous guidelines for the trends adopted in the future.

scholarly journals Designing Localized MPPT for PV Systems Using Fuzzy-Weighted Extreme Learning Machine

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2615 ◽  
Author(s):  
Yang Du ◽  
Ke Yan ◽  
Zixiao Ren ◽  
Weidong Xiao
Keyword(s):  
Machine Learning ◽  
Extreme Learning Machine ◽  
Weather Conditions ◽  
Learning Technologies ◽  
Energy Harvest ◽  
Support Vector ◽  
Pv Systems ◽  
Weighted Extreme Learning Machine ◽  
Learning Machine ◽  
Power Point

A maximum power point tracker (MPPT) should be designed to deal with various weather conditions, which are different from region to region. Customization is an important step for achieving the highest solar energy harvest. The latest development of modern machine learning provides the possibility to classify the weather types automatically and, consequently, assist localized MPPT design. In this study, a localized MPPT algorithm is developed, which is supported by a supervised weather-type classification system. Two classical machine learning technologies are employed and compared, namely, the support vector machine (SVM) and extreme learning machine (ELM). The simulation results show the outperformance of the proposed method in comparison with the traditional MPPT design.

scholarly journals A Machine Learning Approach to Low-Cost Photovoltaic Power Prediction Based on Publicly Available Weather Reports

Energies ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 735 ◽  
Author(s):  
Nailya Maitanova ◽  
Jan-Simon Telle ◽  
Benedikt Hanke ◽  
Matthias Grottke ◽  
Thomas Schmidt ◽  
...  
Keyword(s):  
Machine Learning ◽  
Power Output ◽  
Solar Irradiance ◽  
Learning Algorithm ◽  
Weather Data ◽  
Power Prediction ◽  
Training Set ◽  
Pv Systems ◽  
Main Challenge ◽  
Irradiance Data

A fully automated transferable predictive approach was developed to predict photovoltaic (PV) power output for a forecasting horizon of 24 h. The prediction of PV power output was made with the help of a long short-term memory machine learning algorithm. The main challenge of the approach was using (1) publicly available weather reports without solar irradiance values and (2) measured PV power without any technical information about the PV system. Using this input data, the developed model can predict the power output of the investigated PV systems with adequate accuracy. The lowest seasonal mean absolute scaled error of the prediction was reached by maximum size of the training set. Transferability of the developed approach was proven by making predictions of the PV power for warm and cold periods and for two different PV systems located in Oldenburg and Munich, Germany. The PV power prediction made with publicly available weather data was compared to the predictions made with fee-based solar irradiance data. The usage of the solar irradiance data led to more accurate predictions even with a much smaller training set. Although the model with publicly available weather data needed greater training sets, it could still make adequate predictions.

A tes

2018 ◽  
Vol 1 (3) ◽  
Author(s):  
IJE Manager
Keyword(s):  
Energy Supply ◽  
Fossil Fuels ◽  
Geographical Location ◽  
Weather Conditions ◽  
Past Century ◽  
Solar Pv ◽  
Pv Systems ◽  
Trade Offs ◽  
A Site ◽  
Selection Methodology

In the past century, fossil fuels have dominated energy supply in Indonesia. However, concerns over emissions are likely to change the future energy supply. As people become more conscious of environmental issues, alternatives for energy are sought to reduce the environmental impacts. These include renewable energy (RE) sources such as solar photovoltaic (PV) systems. However, most RE sources like solar PV are not available continuously since they depend on weather conditions, in addition to geographical location. Bali has a stable and long sunny day with 12 hours of daylight throughout the year and an average insolation of 5.3 kWh/m2 per day. This study looks at the potential for on-grid solar PV to decarbonize energy in Bali. A site selection methodology using GIS is applied to measure solar PV potential. Firstly, the study investigates the boundaries related to environmental acceptability and economic objectives for land use in Bali. Secondly, the potential of solar energy is estimated by defining the suitable areas, given the technical assumptions of solar PV. Finally, the study extends the analysis to calculate the reduction in emissions when the calculated potential is installed. Some technical factors, such as tilting solar, and intermittency throughout the day, are outside the scope of this study. Based on this model, Bali has an annual electricity potential for 32-53 TWh from solar PV using amorphous thin-film silicon as the cheapest option. This potential amount to three times the electricity supply for the island in 2024 which is estimated at 10 TWh. Bali has an excessive potential to support its own electricity demand with renewables, however, some limitations exist with some trade-offs to realize the idea. These results aim to build a developmental vision of solar PV systems in Bali based on available land and the region’s irradiation.

Optimizing genotype-environment-management interactions to ensure silage maize production in the Chinese Maize Belt

2020 ◽  
Vol 80 (2) ◽  
pp. 133-146
Author(s):  
L Zhang ◽  
Z Zhang ◽  
J Cao ◽  
Y Luo ◽  
Z Li
Keyword(s):  
Field Experiments ◽  
Weather Conditions ◽  
Growth Cycle ◽  
High Energy ◽  
Risk Level ◽  
Energy Yield ◽  
Maize Production ◽  
Early Date ◽  
Environment Management ◽  
Silage Maize

Grain maize production exceeds the demand for grain maize in China. Methods for harvesting good-quality silage maize urgently need a theoretical basis and reference data in order to ensure its benefits to farmers. However, research on silage maize is limited, and very few studies have focused on its energetic value and quality. Here, we calibrated the CERES-Maize model for 24 cultivars with 93 field experiments and then performed a long-term (1980-2017) simulation to optimize genotype-environment-management (G-E-M) interactions in the 4 main agroecological zones across China. We found that CERES-Maize could reproduce the growth and development of maize well under various management and weather conditions with a phenology bias of <5 d and biomass relative root mean square error values of <5%. The simulated results showed that sowing long-growth-cycle cultivars approximately 10 d in advance could yield good-quality silage. The optimal sowing dates (from late May to July) and harvest dates (from early October to mid-November) gradually became later from north to south. A high-energy yield was expected when sowing at an early date and/or with late-maturing cultivars. We found that Northeast China and the North China Plain were potential silage maize growing areas, although these areas experienced a medium or even high frost risk. Southwestern maize experienced a low risk level, but the low soil fertility limited the attainable yield. The results of this paper provide information for designing an optimal G×E×M strategy to ensure silage maize production in the Chinese Maize Belt.

Adsorption Isotherm Predictions for Multiple Molecules in MOFs Using the Same Deep Learning Model

2019 ◽  
Author(s):  
Ryther Anderson ◽  
Achay Biong ◽  
Diego Gómez-Gualdrón
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Molecular Simulation ◽  
Large Scale ◽  
Learning Model ◽  
Operating Conditions ◽  
Small Subset ◽  
Screening Methods ◽  
Large Set ◽  
Metal Organic

<div>Tailoring the structure and chemistry of metal-organic frameworks (MOFs) enables the manipulation of their adsorption properties to suit specific energy and environmental applications. As there are millions of possible MOFs (with tens of thousands already synthesized), molecular simulation, such as grand canonical Monte Carlo (GCMC), has frequently been used to rapidly evaluate the adsorption performance of a large set of MOFs. This allows subsequent experiments to focus only on a small subset of the most promising MOFs. In many instances, however, even molecular simulation becomes prohibitively time consuming, underscoring the need for alternative screening methods, such as machine learning, to precede molecular simulation efforts. In this study, as a proof of concept, we trained a neural network as the first example of a machine learning model capable of predicting full adsorption isotherms of different molecules not included in the training of the model. To achieve this, we trained our neural network only on alchemical species, represented only by their geometry and force field parameters, and used this neural network to predict the loadings of real adsorbates. We focused on predicting room temperature adsorption of small (one- and two-atom) molecules relevant to chemical separations. Namely, argon, krypton, xenon, methane, ethane, and nitrogen. However, we also observed surprisingly promising predictions for more complex molecules, whose properties are outside the range spanned by the alchemical adsorbates. Prediction accuracies suitable for large-scale screening were achieved using simple MOF (e.g. geometric properties and chemical moieties), and adsorbate (e.g. forcefield parameters and geometry) descriptors. Our results illustrate a new philosophy of training that opens the path towards development of machine learning models that can predict the adsorption loading of any new adsorbate at any new operating conditions in any new MOF.</div>

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