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

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.

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A Comparative Analysis of Machine Learning Techniques for Spam Detection

International Journal of Advanced Research in Science, Communication and Technology ◽

10.48175/ijarsct-1308 ◽

2021 ◽

pp. 657-661

Author(s):

Rashida Ali ◽

Ibrahim Rampurawala ◽

Mayuri Wandhe ◽

Ruchika Shrikhande ◽

Arpita Bhatkar

Keyword(s):

Machine Learning ◽

Natural Language Processing ◽

Comparative Analysis ◽

Natural Language ◽

Language Processing ◽

High Volume ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Spam Detection ◽

Learning Techniques

Internet provides a medium to connect with individuals of similar or different interests creating a hub. Since a huge hub participates on these platforms, the user can receive a high volume of messages from different individuals creating a chaos and unwanted messages. These messages sometimes contain a true information and sometimes false, which leads to a state of confusion in the minds of the users and leads to first step towards spam messaging. Spam messages means an irrelevant and unsolicited message sent by a known/unknown user which may lead to a sense of insecurity among users. In this paper, the different machine learning algorithms were trained and tested with natural language processing (NLP) to classify whether the messages are spam or ham.

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Analysis Of Solar Power Generation Forecasting Using Machine Learning Techniques

E3S Web of Conferences ◽

10.1051/e3sconf/202130901163 ◽

2021 ◽

Vol 309 ◽

pp. 01163

Author(s):

K. Anuradha ◽

Deekshitha Erlapally ◽

G. Karuna ◽

V. Srilakshmi ◽

K. Adilakshmi

Keyword(s):

Machine Learning ◽

Power Generation ◽

Random Forest ◽

Regression Models ◽

Solar Power ◽

Machine Learning Techniques ◽

Pv Systems ◽

Solar Power Generation ◽

Learning Techniques ◽

Solar Power Forecasting

Solar power is generated using photovoltaic (PV) systems all over the world. Because the output power of PV systems is alternating and highly dependent on environmental circumstances, solar power sources are unpredictable in nature. Irradiance, humidity, PV surface temperature, and wind speed are only a few of these variables. Because of the unpredictability in photovoltaic generating, it’s crucial to plan ahead for solar power generation as in solar power forecasting is required for electric grid. Solar power generation is weather-dependent and unpredictable, this forecast is complex and difficult. The impacts of various environmental conditions on the output of a PV system are discussed. Machine Learning (ML) algorithms have shown great results in time series forecasting and so can be used to anticipate power with weather conditions as model inputs. The use of multiple machine learning, Deep learning and artificial neural network techniques to perform solar power forecasting. Here in this regression models from machine learning techniques like support vector machine regressor, random forest regressor and linear regression model from which random forest regressor beaten the other two regression models with vast accuracy.

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Machine learning for transient recognition in difference imaging with minimum sampling effort

Monthly Notices of the Royal Astronomical Society ◽

10.1093/mnras/staa3096 ◽

2020 ◽

Vol 499 (4) ◽

pp. 6009-6017

Author(s):

Y-L Mong ◽

K Ackley ◽

D K Galloway ◽

T Killestein ◽

J Lyman ◽

...

Keyword(s):

Machine Learning ◽

Feature Representation ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Sampling Effort ◽

Training Set ◽

The Real ◽

Learning Techniques ◽

The Difference ◽

Difference Imaging

ABSTRACT The amount of observational data produced by time-domain astronomy is exponentially increasing. Human inspection alone is not an effective way to identify genuine transients from the data. An automatic real-bogus classifier is needed and machine learning techniques are commonly used to achieve this goal. Building a training set with a sufficiently large number of verified transients is challenging, due to the requirement of human verification. We present an approach for creating a training set by using all detections in the science images to be the sample of real detections and all detections in the difference images, which are generated by the process of difference imaging to detect transients, to be the samples of bogus detections. This strategy effectively minimizes the labour involved in the data labelling for supervised machine learning methods. We demonstrate the utility of the training set by using it to train several classifiers utilizing as the feature representation the normalized pixel values in 21 × 21 pixel stamps centred at the detection position, observed with the Gravitational-wave Optical Transient Observer (GOTO) prototype. The real-bogus classifier trained with this strategy can provide up to $95{{\ \rm per\ cent}}$ prediction accuracy on the real detections at a false alarm rate of $1{{\ \rm per\ cent}}$.

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Better Not to Use Vulnerability’s Reference for Exploitability Prediction

Applied Sciences ◽

10.3390/app10072555 ◽

2020 ◽

Vol 10 (7) ◽

pp. 2555 ◽

Cited By ~ 1

Author(s):

Heedong Yang ◽

Seungsoo Park ◽

Kangbin Yim ◽

Manhee Lee

Keyword(s):

Machine Learning ◽

Prediction Performance ◽

Machine Learning Techniques ◽

Proof Of Concept ◽

Release Date ◽

Software Companies ◽

Reference Information ◽

Learning Techniques ◽

Test Sets ◽

The Difference

About half of all exploit codes will become available within about two weeks of the release date of its vulnerability. However, 80% of the released vulnerabilities are never exploited. Since putting the same effort to eliminate all vulnerabilities can be somewhat wasteful, software companies usually use different methods to assess which vulnerability is more serious and needs an immediate patch. Recently, there have been some attempts to use machine learning techniques to predict a vulnerability’s exploitability. In doing so, a vulnerability’s related URL, called its reference, is commonly used as a machine learning algorithm’s feature. However, we found that some references contained proof-of-concept codes. In this paper, we analyzed all references in the National Vulnerability Database and found that 46,202 of them contained such codes. We compared prediction performances between feature matrix with and without reference information. Experimental results showed that test sets that used references containing proof-of-concept codes had better prediction performance than ones that used references without such codes. Even though the difference is not huge, it is clear that references having answer information contributed to the prediction performance, which is not desirable. Thus, it is better not to use reference information to predict vulnerability exploitation.

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ESTIMATING UNDERDIAGNOSIS OF COVID-19 WITH NOWCASTING AND MACHINE LEARNING – EXPERIENCE FROM BRAZIL

10.1101/2020.07.01.20144402 ◽

2020 ◽

Author(s):

Leandro Pereira Garcia ◽

André Vinícius Gonçalves ◽

Matheus Pacheco Andrade ◽

Lucas Alexandre Pedebôs ◽

Ana Cristina Vidor ◽

...

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

Learning Experience ◽

Data Extraction ◽

Entire Period ◽

Machine Learning Techniques ◽

Ecological Design ◽

Learning Techniques ◽

The Difference ◽

South Of Brazil

ABSTRACTBackgroundBrazil has the second largest COVID-19 number of cases, worldly. Even so, underdiagnosis in the country is massive. Nowcasting techniques have helped to overcome the underdiagnosis. Recent advances in machine learning techniques offer opportunities to refine the nowcasting. This study aimed to analyze the underdiagnosis of COVID-19, through nowcasting with machine learning, in a South of Brazil capital.MethodsThe study has an observational ecological design. It used data from 3916 notified cases of COVID-19, from April 14th to June 02nd, 2020, in Florianópolis, Santa Catarina, Brazil. We used machine-learning algorithm to classify cases which had no diagnosis yet, producing the nowcast. To analyze the underdiagnosis, we compared the difference between the data without nowcasting and the median of the nowcasted projections for the entire period and for the six days from the date of onset of symptoms to diagnosis at the moment of data extraction.ResultsThe number of new cases throughout the entire period, without nowcasting, was 389. With nowcasting, it was 694 (UI95 496-897,025). At the six days period, the number without nowcasting was 19 and 104 (95% UI 60-142) with. The underdiagnosis was 37.29% in the entire period and 81.73% at the six days period.ConclusionsThe underdiagnosis was more critical in six days from the date of onset of symptoms to diagnosis before the data collection than in the entire period. The use of nowcasting with machine learning techniques can help to estimate the number of new cases of the disease.

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Method of monitoring and detection of failures in PV system based on machine learning techniques

Revista Facultad de Ingeniería Universidad de Antioquia ◽

10.17533/udea.redin.20200694 ◽

2020 ◽

Author(s):

Dario Javier Benavides ◽

Paul Arévalo-Cordero ◽

Luis G. González ◽

Luis Hernández-Callejo ◽

Francisco Jurado ◽

...

Keyword(s):

Machine Learning ◽

Smart Grids ◽

Energy Production ◽

Machine Learning Techniques ◽

Photovoltaic Systems ◽

Pv System ◽

Electricity Grid ◽

Machine Learning Methods ◽

Scada System ◽

Learning Techniques

Machine learning methods have been used to solve complicated practical problems in different areas and are becoming increasingly popular today. The purpose of this article is to evaluate the prediction of the energy production of three different photovoltaic systems and the supervision of measurement sensors, through Machine learning and data mining in response to the behavior of the climatic variables of the place under study. On the other hand, it also includes the implementation of the resulting models in the SCADA system through indicators, which will allow the operator to actively manage the electricity grid. It also offers a strategy in simulation and prediction in real-time of photovoltaic systems and measurement sensors in the concept of smart grids.

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Using machine learning techniques to reduce data annotation time

PsycEXTRA Dataset ◽

10.1037/e577762012-020 ◽

2006 ◽

Author(s):

Christopher Schreiner ◽

Kari Torkkola ◽

Mike Gardner ◽

Keshu Zhang

Keyword(s):

Machine Learning ◽

Machine Learning Techniques ◽

Data Annotation ◽

Learning Techniques

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Using Machine Learning Algorithms on Prediction of Stock Price

Journal of Modeling and Optimization ◽

10.32732/jmo.2020.12.2.84 ◽

2020 ◽

Vol 12 (2) ◽

pp. 84-99

Author(s):

Li-Pang Chen

Keyword(s):

Machine Learning ◽

Stock Price ◽

Short Term Memory ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Support Vector ◽

Short Term ◽

Learning Techniques ◽

Historical Database ◽

Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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