Research on Clustering and Profiling of Electric Vehicle Users Based on K-Means Method

Rain is one of the hydrological cycles which is a cycle of water rotation from the earth to the atmosphere and back to the earth continuously. High Rainfall may cause some areas that are in lowlands or those with low water infiltration systems will be very susceptible to flooding. For that it is necessary to have a system to classify weather data and rainfall in each city and district so the city that has high rainfall and extreme weather can be given special attention to prevent any natural disaster like flooding. The collected data will be processed with K-Means algorithm to classify the cities or district that have low, medium, high, or very high rainfall data. In the K-Means algorithm the amount of k or cluster usually determined by randomly, on this project will be used a method that is Elbow Method to determine the value of k or cluster and Silhouette Coefficient Method will be used for testing the quality amount of a cluster. The data that will be used is the rainfall data from dataonline.bmkg.go.id at a certain period of time to be classified using the K-Means algorithm. The elbow method and the silhouette method can be used in selecting a good optimal number of clusters, and both methods mostly have the same results in determining the optimal number of clusters, it can be seen that the calculation of accuracy between using the optimal number of clusters is higher rather than not using the amount optimal number of clusters. This can be seen in the results of the clustering in Semarang on February 1 - 28, 2021, when using the amount of K = 4 produce the accuracy result 92.8571429 %, while when using the optimal number of cluster K=3 the accuracy result is higher (97.6190476 %). In the Cilacap city classification on April 1-30 2021, the elbow method and the silhouette coefficient method produce different optimal cluster results, but the accuracy obtained when using the optimal number of clusters from the silhouette coefficient (85.7142857 %) is higher than using the optimal cluster from the elbow method.(74.6031746 %), but when the data is processed with centroid on table 5.10, the elbow method and silhouette coefficient method produce the same amount of optimal number of clusters is 2. This shows that differences in the use of the initial centroid point can affect the results of the elbow method and the silhouette coefficient method

The increasing trend of transportation electrification presents investors the opportunity to provide charging services to Electric Vehicle (EV) owners via the energy purchased from the wholesale electricity market. This will benefit EV owners with the availability of competitive rates compared to the regulated utility time-of-use (TOU) rates. The fundamental questions addressed in this paper are 1) will EV owners benefit from the additional choice of Electric Vehicle Charging Stations (EVCSs) compared to home charging? 2) is there any profitable market opportunity for charging stations while the retail electricity market is regulated? To this end, the strategic bidding problem for EVCSs which purchase electricity from the Day-Ahead Electricity Market (DAM) and sell it to EV owners is presented. The strategic bidding problem is constrained by the market-clearing problem within the DAM as well as EVs' charging cost minimization problem. A bi-level optimization problem formulation and a solution method are presented to address this work's research questions. The effectiveness of the proposed structure in gaining profit for EVCSs is illustrated, and it is shown that EV owners also save on their charging cost with the presence of EVCSs as a choice.

Scheduling model of electric vehicles charging considering inconvenience and dynamic electricity prices

Electric vehicles (EVs) are becoming a popular mode of transportation. However, one of the main concerns for EV and CNG owners is the availability and accessibility of charging stations. In this project, we present the development and implementation of an EV and CNG charging station finder web application. The app uses the Google Maps API to display the location of nearby pay stations and provide details such as connection type, availability and price. The app also allows users to filter charging and CNG stations according to their preferences and report charging stations as false or unserviceable. User testing is done to evaluate the usability and performance of the application. The results show that the app is easy to use and provides accurate and up-to date information on payment facilities In this system, the User is able to manage All his EVs inside the app plus he has search/book as lot in advance in the charging station. The User can also search an EV station based on nearby, city or kilometers. The System also provides a Roadmap if you enter source & destination with the charging stations on the way according to the kilometers entered. Admin will manage all the stations and slots. The need for developing infrastructure such as charging stations is undeniable. This EV Charging Station web application has been developed to help EV drivers locate available charging stations near them. After locating a charging station, users can also book a slot at the station to charge their vehicle. EV owners can also use this system to plan their trips more efficiently. Users simply need to specify the source and destination. Based on these two parameters, this system prepares a roadmap with all available charging stations along the journey.

Many EU countries have used financial incentives to promote electric light commercial vehicles and decarbonise urban freight transport. However, the high costs of electric light commercial vehicles still pose a significant adoption barrier. This paper evaluates the impact of financial incentives on the total cost of ownership of electric light commercial vehicles in the European Union and their role in achieving the cost-competitiveness of electric vehicles. A pairwise comparison is performed for three weight categories of electric and diesel vehicles in twelve countries. Our analysis shows that financial incentives have a high impact on the total cost of ownership and have proven necessary to achieve electric vehicles' cost-competitiveness in most cases. However, in specific cases, financial incentives may no longer be needed as the cost of electric vehicles' ownership would have been lower than diesel vehicles even without them. This paper demonstrates the importance of adjusting government incentives to avoid unnecessary funding allocations while promoting the decarbonisation of light commercial vehicles, particularly as the number of incentives does not directly correlate with electric vehicles penetration.

This paper investigates the day-ahead optimal reserve management problem of electric vehicle (EV) aggregator. Geographically dispersed EVs are coordinated by the aggregator to participate in the day-ahead reserve market. A bilevel model is proposed to formulate the interaction between the aggregator and the EV owners. In the upper level, the EV aggregator aggregates the reserve capacity provided by the EV owners and then bids in the reserve market. In the lower level, the EV owners decide their optimal energy charging/discharging and reserve capacity based on the reserve price released by the aggregator. Compared with existing works, our proposed bilevel model is more practical. To be specific, we take into account the exclusive right constraint for accessing EV battery, i.e., the battery cannot be simultaneously accessed by the EV owner and the aggregator. This practical model leads to a bilevel mixed integer nonlinear program, which is difficult to solve because the lower level problem incorporates nonconvex integer variables. A novel exact algorithm is developed to solve it and the finite convergence is proved. Comprehensive case studies demonstrate the economic merits of our proposed model to both the aggregator and the EV owners. We also compare the solution optimality with the state-of-the-art approach and thus validate the effectiveness of our proposed exact algorithm.

Social media has become a central part of daily life in Indonesia, yet the rapid growth of digital platforms presents challenges in analyzing large, unstructured user data. Responding to this issue, this study aims to cluster the behavioral patterns of Indonesian social media users using the K-Means Clustering algorithm, a data mining technique for unsupervised segmentation. Employing a quantitative approach, data were collected through an online questionnaire from 553 respondents aged 15–80 years. After data cleaning, normalization, and feature encoding, the optimal number of clusters was determined using the Elbow method and Silhouette Coefficient, resulting in two clusters with a Silhouette score of 0.177. Cluster 0 (303 respondents) represents highly interactive multi-platform users active on Instagram, TikTok, and YouTube for 3–6 hours daily, showing strong interest in entertainment and motivational content. Cluster 1 (250 respondents) includes more passive users, mainly on Instagram and TikTok, spending 3–4 hours per day with moderate engagement and a preference for motivational and self-development content. The findings demonstrate that K-Means Clustering effectively maps user behavior based on platform use, content preferences, motivations, and interaction patterns. The implications of these findings suggest that digital communication strategies need to be tailored to the characteristics of each user cluster so that the messages and content conveyed are more effective.

Melasma management is challenged by heterogeneity in patient presentation, particularly among individuals with darker skin tones. This study applied k-means clustering, an unsupervised machine learning algorithm that partitions data into k distinct clusters based on feature similarity, to identify patient subgroups that could provide a hypothesis-generating framework for future precision strategies. We analysed clinical and demographic data from 150 South African women with melasma using k-means clustering. The optimal number of clusters was determined using the Elbow Method and Bayesian Information Criterion (BIC), with t-distributed stochastic neighbour embedding (t-SNE) visualization for assessment. The k-Means algorithm identified seven exploratory patient clusters explaining 52.6% of the data variability (R2 = 0.526), with model evaluation metrics including BIC = 951.630 indicating optimal model fit and a Silhouette Score of 0.200 suggesting limited separation between clusters consistent with overlapping clinical phenotypes, while the Calinski-Harabasz index of 26.422 confirmed relatively well-defined clusters that were characterized by distinct profiles including “The Moderately Sun Exposed Young Women”, “Elderly Women with Long-Term Melasma”, and “Younger Women with Severe Melasma”, with key differentiators being age distribution and menopausal status, melasma severity and duration patterns, sun exposure behaviours, and quality of life impact profiles that collectively define the unique clinical characteristics of each subgroup. This study demonstrates how machine learning can identify clinically relevant patient subgroups in melasma. Aligning interventions with the characteristics of specific clusters can potentially improve treatment efficacy.

Nowadays, environmental pollution and the limitation of fossil fuels are taken into consideration by many countries. Worries over the pollution from fossil fuels in the transportation sector have prompted the inclination to use electric vehicles (EVs) instead of the conventional internal combustion engines. It is predicted that at least 10% of the US transportation fleet will be changed to EVs by 2020, and they could have 50% of vehicle market share by 2050. Furthermore, by increasing the penetration of the renewable energy resources in the power system, we can use these clean energies to charge EVs; in this way, we can solve air pollution and fossil fuels problems. Implementing this procedure needs many infrastructures to handle the charging demand of the EVs properly. It should be mentioned that by increasing the penetration of the EVs in the power system, we are confronted with large load demand by sharp stochastic behavior which can have significant effects on the power system parameters such as voltage variations and power loss. To handle this problem, we need to charge EVs in smart mode by considering the power system constraints. In this way, some internal units between EV owners and power system operator, which are named as aggregators, are considered. These units buy electricity energy from the power system operator and sell it to the customers (EV owners). The main goal of these units is to find the optimal charging procedure of EVs by considering power system limitations and minimizing the EV owners charging cost. Here, the main challenge for aggregators to find the optimal charging solution is to model the EVs travel behavior and estimate the value and time of their charging demand in a precise manner. If they can estimate these parameters accurately, it is highly profitable for them and has a significant effect on their income. In this regard, we need to find the precise approach to forecast the EVs travel behavior with high accuracy which has a potential to handle large dimension data; because in the near future by increasing the penetration of the EVs in the transportation fleet, we will face big data problem. To handle the large dimension data sets, machine learning tasks, which are developed based on artificial intelligence concept, can be regarded as the best solution, and these approaches have acceptable performance on other data engineering tasks such as time series forecasting, image and voice processing, and pattern recognition. In this chapter, we introduce an artificial intelligence-based approach for modeling the EVs travel behavior and describe it in full details.

Social welfare-based charging of electric vehicles in the microgrids fed by renewables

K-Means Algorithm can be used to group tourists based on reviews on tourist destination objects. This algorithm has a weakness that is sensitive to the determination of the initial centroid. The initial centroid that is determined at random will decreasing the level accuracy, often gets stuck at the local optimum, and gets a random solution. Optimization algorithms such as PSO can overcome this by determining the optimal initial centroid. The optimal number of clusters (K) will be determined using the Elbow method by calculating the SSE value of the resulting cluster. The average Silhouette Coefficient (SC) is used to measure the quality of the clusters produced by the K-Means Algorithm with and without the PSO Algorithm. This study uses secondary data obtained from the UCI Machine Learning Repository with the name Travel Reviews Data Set which consists of 980 records and 10 attributes. The test results show that K=2 is the optimal number of clusters. The K-Means and PSO Algorithm gives an average SC value of 0.300358 which is better than without the PSO Algorithm of 0.300076. The optimal PSO hyperparameter generated is the number of particles=30, \varphi_1=2.2, and {\ \varphi}_2=3 at maximum iteration of 100.

Characteristics of Sickle Cell Patients with Frequent ED Visits and Hospitalizations: Demographic, Clinical, Laboratory and Psycho-Social Aspects

Estimating the grid payments necessary to compensate additional costs to prospective electric vehicle owners who provide vehicle-to-grid ancillary services

The concept known as Vehicle-to-Grid (V2G) is to provide power to help balance loads by charging at night when demand is low and sending power to the grid when demand is high. Therefore, it is important to model the cost-benefit characteristics of Electric vehicle(EV)'s operation considering the negawatt market in real time. This paper proposes a methodology to formulate the various costs and economic benefits for sending the EV's power back to the grid, including a concept of inconvenience costs caused by operating the EV as a battery. This paper also introduces the general decision-making process based on the cost-benefit analysis in order to simulate the reasonable participation of V2G service. In the case study, it is confirmed by two-case simulations that the proposed approach is useful to help EV owners' decision-making. In the future, it is expected that the proposed methodology can be used as a decision-making guideline to help prepare the EV' power transmission.

This paper develops a discrete-continuous model to examine the ownership and usage of electric vehicles in the household. The impact of the ownership and usage of ordinary vehicles is taken into consideration. 5766 stated preference data concerning purchasing electric vehicles in the Chukyo region in Japan are utilized as the research sample. The monthly mileages of ordinary and electric vehicles are measured by a Tobit model, respectively. The ordinary vehicle ownership is measured by an ordered probit model, while the electric vehicle ownership is measured by a binary probit model. Gibbs sampler algorithm is used to estimate four jointed equations. The result shows that there is a substitution effect between two types of vehicles in the ownership and usage. The price, capacity, range and charging rate in the gas station impact both the ownership and usage of electric vehicles. Meanwhile, charging time does not affect either the ownership or usage.