scholarly journals Integrated Regression Approach for Prediction of solar irradiance based on multiple weather factors

2021 ◽  
Vol 11 (2) ◽  
pp. 0-0
Keyword(s):  
Solar Energy ◽  
Solar Irradiance ◽  
Central India ◽  
Real Data ◽  
Weather Conditions ◽  
Weather Factors ◽  
Regression Approach ◽  
Different Seasons ◽  
Weather Parameters ◽  
Artificial Neural Network Ann

Solar irradiance is the most vital aspect in estimating the solar energy collection at any location. Renewable energy setup at any location is dependent on it and other ambient weather parameters. However, it is hard to predict due to unstable nature and dependence on variations in weather conditions. The correlation of ambient weather factors on the performance of solar irradiance is analysed, by collecting the data using weather API, over the year for a particular location of central India. The training of this non-linear data is carried out with hybrid regression model integrating decision tree regression with Artificial Neural Network (ANN) module. Experimentation is performed using real data of different days from different seasons of the year, also by considering different irradiance conditions. The results demonstrated significant weather factors with moderate positive and negative correlation with solar irradiance, which can be used as a helpful tool to predict it before deployment of solar energy setup.

scholarly journals SPICE-Aided Modeling of Daily and Seasonal Changes in Properties of the Actual Photovoltaic Installation

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6247
Author(s):  
Krzysztof Górecki ◽  
Jacek Dąbrowski ◽  
Ewa Krac
Keyword(s):  
Seasonal Changes ◽  
Weather Conditions ◽  
Electrical Energy ◽  
Solar Irradiation ◽  
Considerable Influence ◽  
Measurement Results ◽  
Different Seasons ◽  
Weather Parameters ◽  
Thermal Phenomena ◽  
Good Agreement

This article proposes a model of an actual photovoltaic installation situated in the Gdynia Maritime University, Poland. This model is formulated in the form of a SPICE network. In the presented model, the influence of selected weather parameters and thermal phenomena on the properties of the components of this installation are taken into account. The structure of the analyzed installation and the form of the formulated model are both presented. By means of this model, values of the power produced by the installation considered in different seasons and different times of the day are computed. The obtained computation results are compared to the measurement results. Good agreement between the results of measurements and computations is obtained. The obtained results of the investigations confirm the considerable influence of weather conditions, as well as daily and seasonal changes in solar irradiation and the ambient temperature, on the electrical energy produced. In the summer months, a decrease in the energy efficiency of the conversion of solar energy into electrical energy in comparison to the winter months is also visible and can even be twofold.

Design and Construction of A Domestic Solar Power Bank in a Convective Environment

2018 ◽  
Vol 11 (1) ◽  
pp. 16-28 ◽  
Author(s):  
M. E. Emetere ◽  
O.I. Osunlola ◽  
A.D. Adejumo ◽  
A.O. Dauda
Keyword(s):  
Rural Communities ◽  
Solar Energy ◽  
Solar Irradiance ◽  
Solar Power ◽  
Small Scale ◽  
Thermo Electric ◽  
Domestic Use ◽  
Weather Parameters ◽  
Local Materials ◽  
Very High

Objective:This research seeks to solve the problem of storing solar energy in small scale modules for domestic use.Method:The Solar Power Bank (SPB) was constructed with local materials based on their individual properties. The functionality of the SPB was tested in a convective environment. Davis automatic Weather Station (DWS) was used to get the weather parameters (like solar irradiance, solar energy and temperature) for each day the SPB was tested. The maximum solar irradiance for four days (during the experiment) were 220 W/m2, 208 W/m2, 450 W/m2and 900 W/m2. The maximum solar energy was 0.33 J, 03 J, 0.64 J and 1.33 J.Result:The maximum voltage and power obtained from the Solar Power Bank (SPB) was 0.18V and 0.065W respectively. The design showed tremendous heat energy entrapment during solar irradiance peak as the temperature in the SPB was about three times the DWS.Conclusion:It was specifically noted that the convection of the heat transfer that is triggered by the glass shield determines the functionality of the thermo-electric module. This is a clear indication that though the power output may be low to charge the batteries, the prospects of the SPB to operate in convective-rural communities (in tropic region) is very high.

A season for complaints: how does weather affect noise complaints between neighbours?

2021 ◽  
Author(s):  
Siqin Wang ◽  
Yan Liu ◽  
Jonathan Corcoran
Keyword(s):  
Built Environment ◽  
Culturally Diverse ◽  
Large Scale ◽  
Weather Conditions ◽  
Primary Role ◽  
Weather Factors ◽  
Noise Sources ◽  
Administrative Dataset ◽  
Different Seasons ◽  
Study Case

AbstractBoth built environment and natural environment have physiological and psychological effect on human behaviour, which potentially affect their sensitivity and tolerance to surrounding noise, and leads to annoyance, nuisance, distress or overt actions and aggressive behaviours such as noise complaints to people living neighborly. This study aims to explore the extent weather conditions affect the prevalence of noise complaints between neighbours mediated through neighbourhood built environment. Using Brisbane, Australia as a study case, we draw on the large-scale administrative dataset in 2016 to explore the monthly and seasonal variations of noise complaints between neighbours, and employ a step-wise multiple regression to analyse the extent weather factors affect noise complaints. Our findings show that neighbours largely complain about noise made by animals and such complaints most frequently appear in March to May, the autumn season in the South Hemisphere. Built environment plays a primary role on noise complaints and culturally diverse suburbs with less green space tend to have a higher likelihood of neighbour complaints in spring and summer; such a likelihood is further increased by a higher level of wind, humidity, and temperature in a yearly frame. However, the effect of weather on animal and non-animal related noise complaints in different seasons is less consistent. Our findings, to a certain degree, reveal that weather conditions may serve as a psychological moderator to change people’s tolerance and sensitivity on noise, alter their routine activities and exposure to noise sources, and further affect the likelihood of imposing noise complaints between neighbours.

Outdoor yards for sheep during winter – Effects of feed location, roof and weather factors on resting and activity

2011 ◽  
Vol 91 (2) ◽  
pp. 213-220 ◽  
Author(s):  
Grete Helen Meisfjord Jørgensen ◽  
Knut Egil Bøe
Keyword(s):  
Weather Conditions ◽  
Factorial Experiment ◽  
Feeding Time ◽  
Weather Factors ◽  
Limited Effect ◽  
General Behaviour ◽  
Video Recordings ◽  
White Breed ◽  
Instantaneous Sampling ◽  
Weather Parameters

Jørgensen, G. H. M. and Bøe, K. E. 2011. Outdoor yards for sheep during winter – Effects of feed location, roof and weather factors on resting and activity. Can. J. Anim. Sci. 91: 213–220. The aim of this experiment was to investigate the effect of roof cover and location of feed on sheep's use of an outdoor yard under different weather conditions. A 2×2 factorial experiment was conducted with roof covering of outdoor yard (yes or no) and location of feed (indoors or outdoors) in four different pens, each with one of four possible combinations of these factors. Twenty adult ewes of the Norwegian White breed were randomly allotted to four groups with five animals. Weather parameters were automatically recorded. The following behavioural parameters were scored using instantaneous sampling every 15 min throughout 24-h video recordings: location (indoors or outdoors), general behaviour (stand/walk, resting, feeding). Weather factors did not seem to have any large influence on sheep behaviour. A roof covering the outdoor yard increased time spent in the yard, had no effect on feeding time, a limited effect on resting time, but increased the time spent resting outdoors. Locating the feed outdoors increased time spent in the yard, but also increased the time spent resting indoors, indicating that if a dry and comfortable resting area is offered indoors, the feed should be located in the outdoor yard.

scholarly journals EFFECT OF WEATHER FACTORS ON COCONUT AND COP PRODUCTION IN KERALA, INDIA

CORD ◽  
1997 ◽  
Vol 13 (01) ◽  
pp. 34
Author(s):  
K Satyabalan
Keyword(s):  
Monsoon Season ◽  
Weather Conditions ◽  
Southwest Monsoon ◽  
Cold Weather ◽  
Maximum Output ◽  
Weather Factors ◽  
North East ◽  
Different Seasons ◽  
Hot Weather ◽  
Kernel Content

Studies made on the constituents of the coconut fruits harvested from West Coast Tall coconut palms grown under rainfed conditions during the different seasons of the year identified as hot weather season from March to May, southwest monsoon season from June to August, North east monsoon season from September to November and cold weather season from December to February in Kerala, India have indicated that the fruits harvested during the hot weather season arc smaller in size but more in number than those harvested during the other seasons of the year. They have low husk content but more kernel content resulting in more copra content per nut. Shell content also is high in these nuts. In the case of fruits harvested during other seasons of the year the number of nuts obtained is low. They are larger in size and have more husk content but less kernel and copra content per nut. The nut characteristics arc very much influenced by the weather conditions prevailing during the twelve month period of development from the fertilized flower to the mature nut. The studies also show that the effects of the season are not operating in the  same way or to the same extent on the different constituents of the fruit like husk, kernel, shell and copra. This is evident from the variation in the constituents of the fruit. The data indicate that maximum output of kernel, shell and copra are obtained from the palms during the hot weather season.

scholarly journals AnANN Model Trained on Regional Data in the Prediction of Particular Weather Conditions

2021 ◽  
Vol 11 (11) ◽  
pp. 4757
Author(s):  
Aleksandra Bączkiewicz ◽  
Jarosław Wątróbski ◽  
Wojciech Sałabun ◽  
Joanna Kołodziejczyk
Keyword(s):  
Atmospheric Pressure ◽  
Real Life ◽  
Real Data ◽  
Weather Conditions ◽  
Maximum Temperature ◽  
Air Masses ◽  
Weather Forecasts ◽  
Life Problems ◽  
The World ◽  
The City

Artificial Neural Networks (ANNs) have proven to be a powerful tool for solving a wide variety of real-life problems. The possibility of using them for forecasting phenomena occurring in nature, especially weather indicators, has been widely discussed. However, the various areas of the world differ in terms of their difficulty and ability in preparing accurate weather forecasts. Poland lies in a zone with a moderate transition climate, which is characterized by seasonality and the inflow of many types of air masses from different directions, which, combined with the compound terrain, causes climate variability and makes it difficult to accurately predict the weather. For this reason, it is necessary to adapt the model to the prediction of weather conditions and verify its effectiveness on real data. The principal aim of this study is to present the use of a regressive model based on a unidirectional multilayer neural network, also called a Multilayer Perceptron (MLP), to predict selected weather indicators for the city of Szczecin in Poland. The forecast of the model we implemented was effective in determining the daily parameters at 96% compliance with the actual measurements for the prediction of the minimum and maximum temperature for the next day and 83.27% for the prediction of atmospheric pressure.

scholarly journals Prediction of Healing Performance of Autogenous Healing Concrete Using Machine Learning

Materials ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4068
Author(s):  
Xu Huang ◽  
Mirna Wasouf ◽  
Jessada Sresakoolchai ◽  
Sakdirat Kaewunruen
Keyword(s):  
Machine Learning ◽  
Search Algorithm ◽  
Weather Conditions ◽  
Prediction Performance ◽  
Machine Learning Algorithms ◽  
Coefficient Of Determination ◽  
Gradient Boosting ◽  
Support Vector ◽  
Self Healing ◽  
Artificial Neural Network Ann

Cracks typically develop in concrete due to shrinkage, loading actions, and weather conditions; and may occur anytime in its life span. Autogenous healing concrete is a type of self-healing concrete that can automatically heal cracks based on physical or chemical reactions in concrete matrix. It is imperative to investigate the healing performance that autogenous healing concrete possesses, to assess the extent of the cracking and to predict the extent of healing. In the research of self-healing concrete, testing the healing performance of concrete in a laboratory is costly, and a mass of instances may be needed to explore reliable concrete design. This study is thus the world’s first to establish six types of machine learning algorithms, which are capable of predicting the healing performance (HP) of self-healing concrete. These algorithms involve an artificial neural network (ANN), a k-nearest neighbours (kNN), a gradient boosting regression (GBR), a decision tree regression (DTR), a support vector regression (SVR) and a random forest (RF). Parameters of these algorithms are tuned utilising grid search algorithm (GSA) and genetic algorithm (GA). The prediction performance indicated by coefficient of determination (R2) and root mean square error (RMSE) measures of these algorithms are evaluated on the basis of 1417 data sets from the open literature. The results show that GSA-GBR performs higher prediction performance (R2GSA-GBR = 0.958) and stronger robustness (RMSEGSA-GBR = 0.202) than the other five types of algorithms employed to predict the healing performance of autogenous healing concrete. Therefore, reliable prediction accuracy of the healing performance and efficient assistance on the design of autogenous healing concrete can be achieved.

scholarly journals Determination of the Factors Affecting King Abdul Aziz University Published Articles in ISI by Multilayer Perceptron Artificial Neural Network

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 766
Author(s):  
Rashad A. R. Bantan ◽  
Ramadan A. Zeineldin ◽  
Farrukh Jamal ◽  
Christophe Chesneau
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Multilayer Perceptron ◽  
Research Study ◽  
Real Data ◽  
Factors Affecting ◽  
King Abdulaziz University ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Deanship of scientific research established by the King Abdulaziz University provides some research programs for its staff and researchers and encourages them to submit proposals in this regard. Distinct research study (DRS) is one of these programs. It is available all the year and the King Abdulaziz University (KAU) staff can submit more than one proposal at the same time up to three proposals. The rules of the DSR program are simple and easy so it contributes in increasing the international rank of KAU. The authors are offered financial and moral reward after publishing articles from these proposals in Thomson-ISI journals. In this paper, multiplayer perceptron (MLP) artificial neural network (ANN) is employed to determine the factors that have more effect on the number of ISI published articles. The proposed study used real data of the finished projects from 2011 to April 2019.

scholarly journals Mapping and Estimation of Monthly Global Solar Irradiation in Different Zones in Souss-Massa Area, Morocco, Using Artificial Neural Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-19 ◽  
Author(s):  
O. Nait Mensour ◽  
S. Bouaddi ◽  
B. Abnay ◽  
B. Hlimi ◽  
A. Ihlal
Keyword(s):  
Solar Energy ◽  
Solar Irradiation ◽  
Kriging Interpolation ◽  
Solar Pv ◽  
Energy Potential ◽  
Global Solar Irradiation ◽  
Interpolation Technique ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

Solar radiation data play an important role in solar energy research. However, in regions where the meteorological stations providing these data are unavailable, strong mapping and estimation models are needed. For this reason, we have developed a model based on artificial neural network (ANN) with a multilayer perceptron (MLP) technique to estimate the monthly average global solar irradiation of the Souss-Massa area (located in the southwest of Morocco). In this study, we have used a large database provided by NASA geosatellite database during the period from 1996 to 2005. After testing several models, we concluded that the best model has 25 nodes in the hidden layer and results in a minimum root mean square error (RMSE) equal to 0.234. Furthermore, almost a perfect correlation coefficient R=0.988 was found between measured and estimated values. This developed model was used to map the monthly solar energy potential of the Souss-Massa area during a year as estimated by the ANN and designed with the Kriging interpolation technique. By comparing the annual average solar irradiation between three selected sites in Souss-Massa, as estimated by our model, and six European locations where large solar PV plants are deployed, it is apparent that the Souss-Massa area is blessed with higher solar potential.

scholarly journals Identification of solar radiation effect on climatic indicators of the territory of Ukraine

2018 ◽  
Keyword(s):  
Solar Radiation ◽  
Solar Energy ◽  
Air Temperature ◽  
Temperature Regime ◽  
Atmospheric Pressure ◽  
Weather Conditions ◽  
Climatic Conditions ◽  
Practical Significance ◽  
Statistical Dependence ◽  
Physical Processes

Formulation of the problem. Understanding that solar energy is the main source of the majority of biological, chemical and physical processes on Earth, investigation of its influence on different climatic fields allows us to define the features of its space and hour fluctuations. To define radiation and temperature regime of the territory it is necessary to determine climatic features of the spreading surface, which absorbs and will transform solar energy. Considering the fact that modern climatic changes and their consequences cover all components of the system, today there is a problem of their further study for comprehension of atmospheric processes, modeling weather conditions on different territories depending on the properties. The purpose of the article is to determine interrelations between indexes of solar radiation (the Wolf's number) and air temperature, atmospheric pressure on the territory of Ukraine during 1965-2015, their change in space and time. Methods. Correlative method is one of the main methods of a statistical analysis which allows us to receive correlation coefficients of solar radiation variability indexes, air temperature, atmospheric pressure on the territory of the research. This technique estimates the extent of solar radiation influence on temperature regime of the territory and distribution of atmospheric pressure. Results. Coefficients of correlation, which characterize variability of solar radiation indexes, air temperature and atmospheric pressure on the explored territory have been received by means of statistical correlation analysis method. This technique allows us to estimate the degree and nature of solar radiation influence on a temperature regime of the territory and distribution of atmospheric pressure. It has been defined that direct correlative connection between indexes of solar radiation is characteristic of air temperature and atmospheric pressure fields. Significant statistical dependence between incoming solar radiation on the territory of Ukraine and atmospheric pressure has been noted during the spring and autumn periods mainly at the majority of stations. Between indexes of solar radiation and air temperature the inverse correlative connection in winter will be transformed to a direct connection during the spring and summer periods. Scientific novelty and practical significance. Physical processes, which happen in the atmosphere, are characterized by complex interrelations. For further research it is important to define solar radiation value and the extent of influence on climatic conditions.

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