PREDICTION OF DAILY PHOTOVOLTAIC ENERGY PRODUCTION USING WEATHER DATA AND REGRESSION

2021 ◽  
pp. 1-20
Author(s):  
Hüseyin Sarper ◽  
Igor Melnykov ◽  
Lee Anne Martínez
Keyword(s):  
Energy Production ◽  
Weather Prediction ◽  
Model Construction ◽  
Weather Data ◽  
Good Prediction ◽  
Linear Regression Models ◽  
Data Set ◽  
Photovoltaic Energy ◽  
Pv Systems ◽  
Good Prediction Accuracy

Abstract This paper presents linear regression models to predict the daily energy production of three photovoltaic (PV) systems located in southeast Virginia. The prediction is based on daylight duration, sky index, the average relative humidity, and the presence of fog or mist. No other daily weather report components were statistically significant. The proposed method is easy to implement, and it can be used in conjunction with other advanced methods in estimating any given future day’s energy production if weather prediction is available. Data from 2013-2015 was used in the model construction. Model validation was performed using newer (2016, 2017, 2020, and 2021) data not used in the model construction. Results show good prediction accuracy for a simple methodology, free of system parameters, that can be utilized by ordinary photovoltaic energy users. The entire data set can be downloaded using the link provided.

scholarly journals The Combination of Building Greenery and Photovoltaic Energy Production—A Discussion of Challenges and Opportunities in Design

2021 ◽  
Vol 13 (3) ◽  
pp. 1537
Author(s):  
Irene Zluwa ◽  
Ulrike Pitha
Keyword(s):  
Energy Production ◽  
Substrate Surface ◽  
Relevant Literature ◽  
Green Roofs ◽  
Cooling Effect ◽  
Photovoltaic Energy ◽  
Pv Systems ◽  
System A ◽  
Challenges And Opportunities ◽  
Building Surfaces

In the case of building surfaces, the installation of green roofs or green facades can be used to reduce the temperature of the environment and the building. In addition, introducing photovoltaic energy production will help to reduce CO2 emissions. Both approaches (building greenery and photovoltaic energy production) compete, as both of them are located on the exterior of buildings. This paper aims to give an overview of solutions for the combination of building greenery (BG) systems and photovoltaic (PV) panels. Planning principles for different applications are outlined in a guideline for planning a sustainable surface on contemporary buildings. A comprehensive literature review was done. Identified solutions of combinations were systematically analysed and discussed in comparison with additional relevant literature. The main findings of this paper were: (A) BG and PV systems with low sub-construction heights require shallow substrates/low growing plants, whereas in the case of the combination of (a semi)-intensive GR system, a distance of a minimum 60 cm between the substrate surface and lower panel edge is recommended; (B) The cooling effect of the greenery depends on the distance between the PV and the air velocity; (C) if the substrate is dry, there is no evapotranspiration and therefore no cooling effect; (D) A spectrum of different PV systems, sub-constructions, and plants for the combination of BG and PV is necessary and suitable for different applications shown within the publication.

Validation of a Risk Equation Predicting Hemodialysis Arteriovenous Fistula Primary Failure in Elderly

2019 ◽  
Vol 51 (1) ◽  
pp. 17-23
Author(s):  
Joyce Z. Qian ◽  
Mara A. McAdams-DeMarco ◽  
Derek Ng ◽  
Bryan Lau
Keyword(s):  
United States ◽  
Arteriovenous Fistula ◽  
The United States ◽  
Hemodialysis Patients ◽  
Calibration Plot ◽  
Good Prediction ◽  
Validation Data ◽  
Data Set ◽  
Risk Equation ◽  
Good Prediction Accuracy

Background: Choice of vascular access for older hemodialysis patients presents a special challenge since the rate of arteriovenous fistula (AVF) primary failure is high. The Lok’s risk equation predicting AVF primary failure has achieved good prediction accuracy and holds great potential for clinical use, but it has not been validated in the United States older hemodialysis patients. Methods: We assembled a validation data set of 14,892 patients aged 67 years and older who initiated hemodialysis with a central venous catheter between July 1, 2010, and June 30, 2012, and had a subsequent, incident AVF placement from the United States Renal Data System. We examined the external validity of Lok’s model by applying it to this validation data set. The discriminatory accuracy and calibration were evaluated by the concordance index (C-statistics) and calibration plot, respectively. Results: The observed frequency of AVF primary failure varied from 0.45 to 0.53 in hemodialysis patients in the validation data set. The predicted probabilities of AVF primary failure calculated by using the Lok’s risk equation ranged from 0.08 to 0.61, and 77.8, 40.5, and 51.7% of patients were categorized as having high, intermediate, and low risk of AVF primary failure, respectively. The C-statistics of the Lok’s risk equation in the validation data set was 0.53 (95% CI 0.52–0.54). The predicted probabilities of AVF primary failure corresponded poorly with the observed proportions in the calibration plot. Conclusions: When externally applied to a cohort of U.S. older hemodialysis patients, the Lok’s risk equation exhibited poor discrimination and calibration accuracy. It is invalid to use it to predict AVF primary failure. A more complex model with strong predictors is expected to better serve clinical determination for AVF placement in this population.

scholarly journals Machine learning prediction and experimental validation of antigenic drift in H3 influenza A viruses in swine

2020 ◽  
Author(s):  
Michael A. Zeller ◽  
Phillip C. Gauger ◽  
Zebulun W. Arendsee ◽  
Carine K. Souza ◽  
Amy L. Vincent ◽  
...  
Keyword(s):  
Amino Acid ◽  
Regression Models ◽  
Influenza A ◽  
High Throughput Sequencing ◽  
Sequence Data ◽  
Antigenic Drift ◽  
Good Prediction ◽  
Linear Regression Models ◽  
Influenza A Viruses ◽  
Good Prediction Accuracy

ABSTRACTThe antigenic diversity of influenza A virus (IAV) circulating in swine challenges the development of effective vaccines, increasing zoonotic threat and pandemic potential. High throughput sequencing technologies are able to quantify IAV genetic diversity, but there are no accurate approaches to adequately describe antigenic phenotypes. This study evaluated an ensemble of non-linear regression models to estimate virus phenotype from genotype. Regression models were trained with a phenotypic dataset of pairwise hemagglutination inhibition (HI) assays, using genetic sequence identity and pairwise amino acid mutations as predictor features. The model identified amino acid identity, ranked the relative importance of mutations in the hemagglutinin (HA) protein, and demonstrated good prediction accuracy. Four previously untested IAV strains were selected to experimentally validate model predictions by HI assays. Error between predicted and measured distances of uncharacterized strains were 0.34, 0.70, 2.19, and 0.17 antigenic units. These empirically trained regression models can be used to estimate antigenic distances between different strains of IAV in swine using sequence data. By ranking the importance of mutations in the HA, we provide criteria for identifying antigenically advanced IAV strains that may not be controlled by existing vaccines and can inform strain updates to vaccines to better control this pathogen.

Improve short-term precipitation forecasts using numerical weather prediction model output and machine learning

2021 ◽  
Author(s):  
Yuhang Zhang ◽  
Aizhong Ye
Keyword(s):  
Machine Learning ◽  
Time Window ◽  
Weather Prediction ◽  
Machine Learning Algorithms ◽  
Precipitation Forecast ◽  
Linear Regression Models ◽  
Post Processing ◽  
Forecast System ◽  
China Meteorological Administration ◽  
Data Set

<p>The hydrological forecasting system coupled with precipitation forecasting can bring us a longer forecast period of early warning information, but it is also accompanied by higher uncertainty. With the improvement of hydrological models, the precipitation forecast may be the largest source of uncertainty. Therefore, before incorporating it into the hydrological model, the precipitation forecast needs post-processing to reduce its uncertainty. Meteorological post-processing corrects the bias of future precipitation forecasts by establishing a linear or non-linear relationship between historical observation and simulation. Machine learning (ML) can fit this relationship and process higher-dimensional predictor features, which is a promising method to improve the accuracy of precipitation forecasts. In this study, we selected the Yalong River basin of China as the cast study and compared the performance of 20 different machine learning algorithms (e.g., ridge regression, random forest, and artificial neural network). The daily hindcast data (1985-2018) from NOAA’s Global ensemble forecast system and corresponding observations from the China Meteorological Administration were selected to construct our data set. To improve the accuracy of the precipitation forecasts, we also screened different combinations of predictors to optimize the model configuration of machine learning, including space, time, and ensemble members. Comparative experiments show that all ML models can improve the accuracy of the raw precipitation forecast, but the performance is different. The extra-trees model has the best results, followed by LightGBM. However, linear regression models perform relatively poorly. The predictor combination of 11 ensemble members and a 2-day time window can achieve the best precipitation forecast. The post-processing of precipitation forecasts based on ML can significantly improve the accuracy of the raw forecasts, and it can also help us build a more advanced hydrological forecast system. In addition, the conclusions of this study and experimental design methods can provide references for the same type of research.</p>

scholarly journals Research on intelligent early warning system of rail transit line trip based on pscada

2021 ◽  
Vol 2113 (1) ◽  
pp. 012079
Author(s):  
Yuqing Zhang ◽  
Xiaohong Zhang
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Weather Data ◽  
Support Vector ◽  
Rail Transit ◽  
Good Prediction ◽  
Data Set ◽  
The Real

Abstract In this paper, an intelligent early warning scheme for rail transit line trip based on PSCADA system is proposed. The scheme takes into account the defects of low prediction accuracy and real-time prediction caused by the lack of power data in the traditional line trip prediction method. At the same time, a large number of power data generated by PSCADA system in the long-term application process are ignored in the field of rail transit[1]. Based on this situation, the prediction data set is constructed by combining the historical power data collected by PSCADA system in rail transit and the lightning weather data in traditional prediction methods. On this basis, the lightgbm machine learning intelligent algorithm is used to compare the similar support vector machine (SVM) and logistic regression algorithm to obtain a model with good prediction effect. In practical application, the real-time data set is constructed by using the real-time power data and real-time weather data collected by PSCADA system to predict, and an intelligent early warning system with the dual advantages of real-time and high accuracy is obtained.

scholarly journals A Computational Protocol Combining DFT and Cheminformatics for Prediction of pH-Dependent Redox Potentials

Molecules ◽  
2021 ◽  
Vol 26 (13) ◽  
pp. 3978
Author(s):  
Rocco Peter Fornari ◽  
Piotr de Silva
Keyword(s):  
Organic Molecules ◽  
Ph Value ◽  
Computational Cost ◽  
Legendre Transform ◽  
Good Prediction ◽  
Redox Potentials ◽  
Free Energies ◽  
Hybrid Functional ◽  
Data Set ◽  
State Changes

Discovering new materials for energy storage requires reliable and efficient protocols for predicting key properties of unknown compounds. In the context of the search for new organic electrolytes for redox flow batteries, we present and validate a robust procedure to calculate the redox potentials of organic molecules at any pH value, using widely available quantum chemistry and cheminformatics methods. Using a consistent experimental data set for validation, we explore and compare a few different methods for calculating reaction free energies, the treatment of solvation, and the effect of pH on redox potentials. We find that the B3LYP hybrid functional with the COSMO solvation method, in conjunction with thermal contributions evaluated from BLYP gas-phase harmonic frequencies, yields a good prediction of pH = 0 redox potentials at a moderate computational cost. To predict how the potentials are affected by pH, we propose an improved version of the Alberty-Legendre transform that allows the construction of a more realistic Pourbaix diagram by taking into account how the protonation state changes with pH.

Challenging the Link Between Early Childhood Television Exposure and Later Attention Problems: A Multiverse Approach

2021 ◽  
pp. 095679762097165
Author(s):  
Matthew T. McBee ◽  
Rebecca J. Brand ◽  
Wallace E. Dixon
Keyword(s):  
Early Childhood ◽  
Regression Models ◽  
Propensity Score Analysis ◽  
Harmful Effect ◽  
Attention Problems ◽  
Linear Regression Models ◽  
Data Set ◽  
Logistic Regression Models ◽  
Score Analysis ◽  
Television Exposure

In 2004, Christakis and colleagues published an article in which they claimed that early childhood television exposure causes later attention problems, a claim that continues to be frequently promoted by the popular media. Using the same National Longitudinal Survey of Youth 1979 data set ( N = 2,108), we conducted two multiverse analyses to examine whether the finding reported by Christakis and colleagues was robust to different analytic choices. We evaluated 848 models, including logistic regression models, linear regression models, and two forms of propensity-score analysis. If the claim were true, we would expect most of the justifiable analyses to produce significant results in the predicted direction. However, only 166 models (19.6%) yielded a statistically significant relationship, and most of these employed questionable analytic choices. We concluded that these data do not provide compelling evidence of a harmful effect of TV exposure on attention.

scholarly journals A Comparative Assessment for the Potential Energy Production from PV Installation on Residential Buildings

2020 ◽  
Vol 12 (24) ◽  
pp. 10344
Author(s):  
Sameh Monna ◽  
Adel Juaidi ◽  
Ramez Abdallah ◽  
Mohammed Itma
Keyword(s):  
Potential Energy ◽  
Energy Production ◽  
Residential Buildings ◽  
Electricity Consumption ◽  
Residential Building ◽  
Electricity Production ◽  
Energy Sustainability ◽  
Pv Systems ◽  
Residential Units ◽  
Future Consumption

This paper targets the future energy sustainability and aims to estimate the potential energy production from installing photovoltaic (PV) systems on the rooftop of apartment’s residential buildings, which represent the largest building sector. Analysis of the residential building typologies was carried out to select the most used residential building types in terms of building roof area, number of floors, and the number of apartments on each floor. A computer simulation tool has been used to calculate the electricity production for each building type, for three different tilt angles to estimate the electricity production. Tilt angle, spacing between the arrays, the building shape, shading from PV arrays, and other roof elements were analyzed for optimum and maximum electricity production. The electricity production for each household has been compared to typical household electricity consumption and its future consumption in 2030. The results show that installing PV systems on residential buildings can speed the transition to renewable energy and energy sustainability. The electricity production for building types with 2–4 residential units can surplus their estimated future consumption. Building types with 4–8 residential units can produce their electricity consumption in 2030. Building types of 12–24 residential units can produce more than half of their 2030 future consumption.

scholarly journals Is Investing in Companies Manufacturing Solar Components a Lucrative Business? A Decision Tree Based Analysis

Energies ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 499
Author(s):  
Sebastian Klaudiusz Tomczak ◽  
Anna Skowrońska-Szmer ◽  
Jan Jakub Szczygielski
Keyword(s):  
Renewable Energy ◽  
Energy Production ◽  
Manufacturing Sector ◽  
Energy Systems ◽  
Product Portfolio ◽  
New Approach ◽  
Renewable Energy Systems ◽  
Pv Systems ◽  
Depth Analysis ◽  
A Company

In an era of increasing energy production from renewable sources, the demand for components for renewable energy systems has dramatically increased. Consequently, managers and investors are interested in knowing whether a company associated with the semiconductor and related device manufacturing sector, especially the photovoltaic (PV) systems manufacturers, is a money-making business. We apply a new approach that extends prior research by applying decision trees (DTs) to identify ratios (i.e., indicators), which discriminate between companies within the sector that do (designated as “green”) and do not (“red”) produce elements of PV systems. Our results indicate that on the basis of selected ratios, green companies can be distinguished from the red companies without an in-depth analysis of the product portfolio. We also find that green companies, especially operating in China are characterized by lower financial performance, thus providing a negative (and unexpected) answer to the question posed in the title.

Simulation of Machining of Incoloy 907 Based on Thermodynamical Constitutive Equation

2013 ◽  
Vol 631-632 ◽  
pp. 681-685
Author(s):  
Fang Shao ◽  
Fa Qing Li ◽  
Hai Ying Zhang ◽  
Xuan Gao
Keyword(s):  
Cutting Force ◽  
Cutting Temperature ◽  
Tool Material ◽  
High Reactivity ◽  
Good Prediction ◽  
Workpiece Material ◽  
Element Analysis ◽  
Good Prediction Accuracy ◽  
Experimental Cutting ◽  
Cutting Tool Materials

Aero-engine alloys (also as known as superalloys)are known as difficult-to-machine materials, especially at higher cutting speeds, due to their several inherent properties such as low thermal conductivity and their high reactivity with cutting tool materials. In this paper a finite element analysis (FEA) of machining for Incoloy907 is presented. In particular, the thermodynamical constitutitve equation(T-C-E) in FEA is applied for both workpiece material and tool material. Cutting temperature and cutting force are predicted. The comparison between the predicted and experimental cutting temperature and cutting force are presented and discussed. The results indicated that a good prediction accuracy of both principal cutting temperature and cutting force can be achieved by the method of FEA with thermodynamical constitutitve equation.

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