scholarly journals Multi-Dimensional Analysis of Load Characteristics of Electrical Vehicles Based on Power Supply Side Data and Unsupervised Learning Method

2021 ◽  
Vol 12 (3) ◽  
pp. 125
Author(s):  
Ziqi Zhang ◽  
Xueliang Huang ◽  
Hongen Ding ◽  
Zhenya Ji ◽  
Zhong Chen ◽  
...  
Keyword(s):  
Unsupervised Learning ◽  
Power Supply ◽  
Weather Conditions ◽  
Evaluation Index System ◽  
Supply Side ◽  
Self Organizing Map ◽  
Learning Method ◽  
Operating Data ◽  
Smart Charging ◽  
Electrical Vehicle

This study set out to extract the charging characteristics of an electrical vehicle (EV) from massive real operating data. Firstly, an unsupervised learning method based on self-organizing map (SOM) is developed to deal with the power supply side data of various charging operators. Secondly, a multi-dimensional evaluation index system is constructed for charging operation and vehicle-to-grid (V2G). Finally, according to more than five million pieces of charging operating data collected over a period of two years, the charging load composition and characteristics under different charging station types, daily types and weather conditions are analyzed. The results show that bus, high-way, and urban public charging loads are different in concentration and regulation flexibility, however, they all have the potential to synergy with power grid and cooperate with renewable energy. Especially in an urban area, more than 37 GWh of photovoltaic (PV) power can be consumed by smart charging at the current penetration rate of EVs.

scholarly journals Precipitation Modeling for Extreme Weather Based on Sparse Hybrid Machine Learning and Markov Chain Random Field in a Multi-Scale Subspace

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1241
Author(s):  
Ming-Hsi Lee ◽  
Yenming J. Chen
Keyword(s):  
Machine Learning ◽  
Markov Chain ◽  
Random Field ◽  
Long Range ◽  
Weather Conditions ◽  
Extreme Weather ◽  
Prediction Algorithm ◽  
Learning Method ◽  
Multi Scale ◽  
Hybrid Machine

This paper proposes to apply a Markov chain random field conditioning method with a hybrid machine learning method to provide long-range precipitation predictions under increasingly extreme weather conditions. Existing precipitation models are limited in time-span, and long-range simulations cannot predict rainfall distribution for a specific year. This paper proposes a hybrid (ensemble) learning method to perform forecasting on a multi-scaled, conditioned functional time series over a sparse l1 space. Therefore, on the basis of this method, a long-range prediction algorithm is developed for applications, such as agriculture or construction works. Our findings show that the conditioning method and multi-scale decomposition in the parse space l1 are proved useful in resisting statistical variation due to increasingly extreme weather conditions. Because the predictions are year-specific, we verify our prediction accuracy for the year we are interested in, but not for other years.

Hybrid Photovoltaic-Diesel Energy System Optimization (Case Study of Electric Power Supply for Buildings under the Weather Conditions of Montenegro)

2014 ◽  
Vol 627 ◽  
pp. 357-364 ◽  
Author(s):  
Goran Radovic ◽  
Vera Murgul ◽  
Nikolai Vatin ◽  
Ekaterina Aronova
Keyword(s):  
Power Systems ◽  
Electric Power ◽  
Power Supply ◽  
Optimal Solution ◽  
Weather Conditions ◽  
System Optimization ◽  
Energy System ◽  
Energy Performance ◽  
Electric Power Supply ◽  
Continuous Power

The article deals with the concept of solar photovoltaic systems use in power supply systems. An analysis of local solar resources potential has been carried out, and optimal orientation points of radiant heat absorbing photovoltaic panels have been chosen to achieve maximum energy performance. Simulation of electric power systems having different configurations has been implemented using the software program Homer. It has been stated that a combination of solar and diesel energy systems is considered to be an optimal solution under the weather conditions of Montenegro. The systems working together make it possible to reduce maintenance costs significantly and adjust capacity generation schedule with due account for energy consumption features to a maximum extent. This allows generating electric power at less cost and results in a more reliable and continuous power supply without failures for a consumer chosen.

scholarly journals A Study on Unsupervised Learning Method of RAM-based Neural Net

2011 ◽  
Vol 16 (1) ◽  
pp. 31-38
Author(s):  
Sang-Moo Park ◽  
Seong-Jin Kim ◽  
Dong-Hyung Lee ◽  
Soo-Dong Lee ◽  
Cheol-Young Ock
Keyword(s):  
Unsupervised Learning ◽  
Neural Net ◽  
Learning Method

Research on Power Quality Evaluation Based on Matter-Element Extension Model

2013 ◽  
Vol 411-414 ◽  
pp. 2735-2741 ◽  
Author(s):  
Yong Hua Wang ◽  
Guo Liang Luo
Keyword(s):  
Power Quality ◽  
Power Supply ◽  
Analytical Hierarchy Process ◽  
Quality Evaluation ◽  
Comprehensive Evaluation ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Extension Model ◽  
Index Value ◽  
Hierarchy Process

Based on the matter-element extension model, this paper first builds a set of reasonable power quality evaluation index system, and then determines the index value through the analytical hierarchy process (AHP), and finally makes a comprehensive evaluation of power supply quality. With an example based on, this paper will prove the feasibility of matter-extension model supplied in power quality evaluation.

Semantics-Based Document Categorization Employing Semi-Supervised Learning

2015 ◽  
pp. 112-140 ◽  
Author(s):  
Jan Žižka ◽  
František Dařena
Keyword(s):  
Machine Learning ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Real World ◽  
Supervised Machine Learning ◽  
The Internet ◽  
Learning Method ◽  
Label Information ◽  
Document Categorization

The automated categorization of unstructured textual documents according to their semantic contents plays important role particularly linked with the ever growing volume of such data originating from the Internet. Having a sufficient number of labeled examples, a suitable supervised machine learning-based classifier can be trained. When no labeling is available, an unsupervised learning method can be applied, however, the missing label information often leads to worse classification results. This chapter demonstrates a method based on semi-supervised learning when a smallish set of manually labeled examples improves the categorization process in comparison with clustering, and the results are comparable with the supervised learning output. For the illustration, a real-world dataset coming from the Internet is used as the input of the supervised, unsupervised, and semi-supervised learning. The results are shown for different number of the starting labeled samples used as “seeds” to automatically label the remaining volume of unlabeled items.

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