Commercial Customers Research Articles

Deep Neural Network-Based Business Data Classification in Intelligent Business Management

The purpose of this paper is to explore how intelligent data mining technology can be used to improve the customer service capability of commercial companies. Based on extensive research on commercial business, this paper uses data mining and machine learning techniques to build an overall framework for applying intelligent technologies to business improvement, and uses multilayer perceptrons and integrated learning algorithms to build classifiers for customer segmentation; uses association rule mining to assist commercial companies in business decisions; uses clustering algorithms and visualization techniques to further analyze claims cases and assist in commercial fraud detection. The multilayer perceptron classification makes the classification of commercial customers more detailed and reasonable, and the company’s business staff can sell products in a more targeted manner; association rule mining greatly improves the quality and efficiency of the company’s management’s decision making.

Assessing credit risk of commercial customers using hybrid machine learning algorithms

Given the large amount of customer data available to financial companies, the use of traditional statistical approaches (e.g., regressions) to predict customers’ credit scores may not provide the best predictive performance. Machine learning (ML) algorithms have been explored in the credit scoring literature to increase predictive power. In this paper, we predict commercial customers’ credit scores using hybrid ML algorithms that combine unsupervised and supervised ML methods. We implement different approaches and compare the performance of the hybrid models to that of individual supervised ML models. We find that hybrid models outperform their individual counterparts in predicting commercial customers’ credit scores. Further, while the existing literature ignores past credit scores, we find that the hybrid models’ predictive performance is higher when these features are included.

Optimization of ESS Scheduling for Cost Reduction in Commercial and Industry Customers in Korea

Various attempts have been made to reduce carbon emissions in the energy sector as part of global net zero emissions trends. Among them, interest in the use of energy storage systems (ESSs) for energy efficiency is growing. Utilities intend to improve the efficiency of investment and operating costs by reducing the maximum peak and leveling the load. Many ESS-scheduling optimization techniques have been studied to reduce the peak demand, balance the load, or reduce the cost corresponding to these two purposes from the customer’s point of view. In this paper, a method for cost minimization that simultaneously considers both the peak demand and load balancing is proposed, and the results and analysis of a case study in Korea Electric Power Corporation (KEPCO), Korea are presented. Through these results, we show that there is a priority among the objective functions of the ESS schedule, that demand charge is more important than energy charge, and that the ESS schedule problem for customers to reduce costs is also beneficial to power system operation by the utility’s rate policy.

Cashless Policy of Nepalese Commercial Banks and Customer Satisfaction

This paper attempts to examine customer satisfaction towards cashless policy in Nepalese commercial banks. The data has been collected from 384 respondents of Kathmandu Valley through structured questionnaire. Descriptive statistics, correlation coefficient and regression methods have been used to estimate the relationship between dependent and independent variables. The study reveals that cashless policy is the most important factor affecting customer satisfaction in Nepalese commercial banks.

The Impact of Access to Finance on Household Welfare: Revisiting the Evidence from Uganda

Our paper highlights the role played by semiformal financial institutions in the livelihoods of Ugandan households that are not served by the commercial banks and microfinance institutions. Data from Living Standards Measurement Survey 2018 Uganda are used. We employ Propensity Score Matching and complement this with the two-step IV Treatment Effects method. We compare the effect of access to services from banks, Village Savings and Loans Associations, Rotating Savings and Credit Associations, and Savings and Credit Cooperatives on household welfare. We find a positive and statistically significant impact of access to both bank and semiformal financial services on household dietary diversity score; school enrollment rates; and clothing expenditure. The improvement in household welfare due to VSLAs, ROSCAs and SACCOs is comparable to that conferred by banks upon their customers. The results are robust to the method of estimation and we show that confounding factors are not a serious problem in our average treatment of the treated estimations. We also examine only those households that: (i) experienced drought; (ii) experienced drought, pests and heavy rains; (iii) were rural; (iv) had farming as main occupation; (v) were male-headed; (vi) had an education level below sample average. We find that in all cases bank, VSLA, ROSCA and SACCO member households have higher household dietary diversity score, school enrollment rates and clothing expenditure than control households. The policy implication from this study is that the improvement in welfare due to VSLAs, ROSCAs and SACCOs is relatively comparable to that obtained by commercial bank customers. Thus there is need to increase the geographic spread and depth of financial outreach of these semiformal institutions to increase the scale of financial inclusion in Uganda.

Indirect Impact of the COVID-19 Pandemic on Natural Gas Consumption by Commercial Consumers in a Selected City in Poland

In March 2020, a lockdown was imposed due to a global pandemic, which contributed to changes in the structure of the consumption of natural gas. Consumption in the industry and the power sector decreased while household consumption increased. There was also a noticeable decrease in natural gas consumption by commercial consumers. Based on collected data, such as temperature, wind strength, duration of weather events, and information about weather conditions on preceding days, models for forecasting gas consumption by commercial consumers (hotels, restaurants, and businesses) were designed, and the best model for determining the impact of the lockdown on gas consumption by the above-mentioned consumers was determined using the MAPE (mean absolute percentage error). The best model of artificial neural networks (ANN) gave a 2.17% MAPE error. The study found a significant decrease in gas consumption by commercial customers during the first lockdown period.

Construction of Online Agricultural Product Consumption Demand Analysis Model Based on Data Mining Technology

The traditional offline marketing mode of agricultural products was relatively single and difficult to adapt to the effective communication between agricultural and commercial enterprises and customers under the new situation, while the coordination between online agricultural products marketing management and customers’ consumption needs was not enough. Therefore, this paper puts forward the research on the construction of online agricultural product consumption demand analysis model based on data mining technology. Based on the supply-demand relationship between agricultural and commercial enterprises and consumers, this paper analyzed consumers and their demand behavior for agricultural products and gave an evaluation model of customers’ consumption demand for agricultural products. Using data mining technology, this paper made a cluster analysis on the characteristics of consumer demand behavior and established the mapping relationship between agricultural products and consumer demand. Then, the online consumption demand prediction and analysis model of agricultural products was put forward. Finally, the performance of demand forecasting model based on data classification was analyzed. The empirical results showed that the model proposed in this paper can better predict and analyze the consumption demand behavior of online agricultural products and had certain feasibility and effectiveness. The online consumption demand analysis model proposed in this paper played a positive role in improving the marketing management level of agricultural products.

Mitigation of voltage sags in distribution line system using static VAR compensator and static synchronous compensator

Voltage sag is one of the power quality disturbances most frequently by customers because it can cause economic loss for the customers especially industries and commercial customers. The main source of voltage sags are faults in transmission and distribution beside two others namely motor large starting and transformer energizing, both the voltage sags have less effect on the equipment. Voltage sags can cause degradation performance of the equipment, and it depends on the magnitude and the duration. The voltage sags that occur in the power system can be compensated with installed Static VAR compensator (SVC) and Static synchronous compensator (STATCOM) in the distribution system bus. The purpose of the study is to compare both types of the device used to mitigate voltage sags in simulation using Alternative transient program (ATP) software. The voltage sag results from the short circuit faults. The method proposes the installation of the two compensator devices alternatively at one of the IEEE thirteen busses systems. The result shows the STATCOM is better than the SVC to handle mitigation of voltage sags compared with the SVC.

A High-Performance Customer Churn Prediction System Based on Self-Attention

Customer churn prediction is a challenging domain of research that contributes to customer retention strategy. The predictive performance of existing machine learning models, which are often adopted by churn communities, appear to be at a bottleneck, partly due to models' poor feature extraction capability. Therefore, a novel algorithm, a hybrid neural network with self-attention enhancement (HNNSAE), is proposed in this paper to improve the efficiency of feature screening and feature extraction, consequently improving the model's predictive performance. This model consists of three main blocks. The first block is the entity embedding layer, which is employed to process the categorical variables transformed into 0-1 code. The second block is the feature extractor, which extracts the significant features through the multi-head self-attention mechanism. In addition, to improve the feature extraction effect, we stack the residual connection neural network on multi-head self-attention modules. The third block is a classifier, which is a three-layer multilayer perceptron. This work conducts experiments on publicly available dataset related to commercial bank customers. The result demonstrates that HNNSAE significantly outperforms the other Individual Machine Learning (IML), Ensemble Machine Learning (EML), and Deep Learning (DL) methods tested in this paper. Furthermore, we compare the performance of the feature extractor proposed in this paper with that of other three feature extractors and find that the method proposed in this paper significantly outperforms other methods. In addition, four hypotheses about model prediction performance and overfitting risk are tested on the publicly available dataset.

Changing Electricity Tariff – Empirical Analysis Based on Commercial Customers Data from Poland

Changing Electricity Tariff – Empirical Analysis Based on Commercial Customers Data from Poland

Optimized CUK Converter Based 1Φ Grid Tied Photovoltaic System

Renewable energy-based power generation, particularly photovoltaic (PV)-connected grid systems, has gained popularity in recent years due to its widespread adoption for residential and commercial customers of all sizes, from kilowatt (KW) to megawatt (MW). The purpose of this work is to demonstrate how an efficient CUK-integrated boost converter with continuous current flow may be used to maximise the output of solar arrays. The constant voltage at the converter output is maintained with increased dynamic performance using a Proportional Integral (PI) controller based on a hybrid optimization technique GWO-PSO (Grey Wolf Optimization-Particle Swarm Optimization). This hybrid solution permits accurate and speedy monitoring of the PI controller, which reduces voltage ripples successfully. This system is connected to the 1 grid via a voltage source inverter (VSI). To ensure grid synchronisation, the inverter’s command pulses are generated by a PI controller depending on the grid’s actual power. The Total Harmonic Distribution (THD) of grid current is used to characterize the overall system. The THD is determined to be 1.8 percent with the suggested PI controller and 3.41 percent with the PI controller, and the tracking performance of the GWO-PSO algorithm is 99.8 percent.

Analyzing Reliability of CGS Station by Continuous Time Markov Chains (CTMC)

Improving the system's reliability is one way to achieve a secure system. City Gas Station (CGS) has a key role in the timely and safe supply of Natural gas (NG) to residential, commercial, and industrial customers. With complexities inherent in systems, having a proper and all-embracing model of the entirety of a system is not readily possible. The continuous-time Markov chain (CTMC) model is regarded as a great help in communicating, comparing, and integrating partial system models. In this study, we have exploited CTMC for reliability analysis in CGS stations. The CTMC model can solve both time-dependent and stationary state probabilities. Therefore, it can potentially develop the state enumeration method into a sequential one. Implementing this procedure leads to identifying critical components and failure probability, eventually enhancing the station's reliability. Additionally, some suggestions are presented for optimizing the performance of the station.

DC Communities: Transformative Building Blocks of the Emerging Energy Infrastructure

Serious environmental concerns call for revolutionary solutions to cope with the harmful effects of the conventional energy landscape. Therefore, residential and commercial customers require cleaner and more reliable energy sources as they become more dependent on energy for daily and critical needs. In this case, transitioning to a cleaner energy economy is of paramount importance for both the environment and the utilities as well as the end-users. The desired transformation will require the deployment of massive amounts of clean energy sources. Many of these resources, such as solar photovoltaic (PV), provide electricity in the form of direct current (DC) that enables the return of DC grids to the electric power arena. The electric system has slowly transitioned to DC, mainly on the demand side. In recent years, modern electronic devices, lighting systems, and an increased number of appliances (≈22% of the residential and commercial loads) have adopted DC systems. Studies suggest that DC loads would account for more than 50% of the available loads in the next few years. Furthermore, the growing proliferation of electric vehicles influx is another example of a successful DC application. From this perspective, the viability of returning to the DC distribution system in the form of DC community grids is explored. We start by defining the DC community grid, which is followed by introducing the benefits of adopting DC at the distribution level. Finally, a summarizing outlook of successful pilot cases, projections of DC community deployment, barriers and concerns, strategies to address barriers and concerns, and suggestions for future research directions are presented. This perspective could shed new light on the building blocks of the transformed energy landscape for various stakeholders.

CUSTOMER SEGMENTATION AND TARGETING FOR DEMAND RESPONSE IN THAILAND

Demand response (DR) refers to utility programs that encourage customers to shift their electric usage during times of high demand on the electric grid. It is a widely used technique to minimize the peak to average consumption ratio. Thus, targeting the right customers becomes important because each program enrollment has costs including marketing enrollment and the setup of enabling hardware at the customer sites. In Thailand, DR events have started since 2014 but the evidences suggest that DR events have not been as successful as they were expected. Most enrolled customers cannot achieve the given curtailment target. However, with the rapid integration of Automatic Meter Reading (AMR), utilities can have a better understanding of electrical consumption behaviour and significantly change how DR targeting is done. In this paper, the situation and details of Thailand's DR are presented. Also, the paper performed customer daily load profiling analysis by using K-Means clustering algorithm in order to select potential groups of customers who can collectively achieve 100 MW load curtailment target. Actual customers load profile data includes 15-minute kilowatt values for 6,050 commercial and industrial customers from 2018 to 2020. Results show that three groups of customers are recommended for DR event participation.

ANALYSIS OF COMMERCIAL CUSTOMER SATISFACTION LEVEL IN PT PERUSAHAAN GAS NEGARA (PERSERO) TBK SBU DISTRIBUSI WILAYAH III SUMATERA BAGIAN UTARA

This study examined of Commercial Customer Satisfaction Level In PT Perussahaan Gas Negara (Persero) Tbk, SBU Distribusi Wilayah III Sumatera Bagian Utara. . The study used descriptive statistical analysis using cartesian diagram which divided into four parts bounded by two lines that intersect perpendicularly at points X and Y. This diagram is also called the importance-performance matrix is used to describe the priority dimension of service quality. or service attributes that need to be improved and serve as a guide for formulating improvement strategies based on decrease in the number of customers in the commercial customer segment from 579 customers in 2012 to 540 customers in 2017, while customers in other segments, namely industry and households increased. The population in this study is all commercial customers in the State Gas Company (Persero) Tbk SBU Distribusi Region III in Medan City as many as 540 commercial customers during the period of 2018. Sampling technique in this study by using probability sampling method with simple random sampling technique. From the results of the research and discussion described in the previous chapter, it is concluded that based on the Descriptive Analysis and Cartesian Diagram Analysis that the influence of service quality consists of: tangible (direct evidence), reliability (reliability), responsiveness (responsiveness), assurance (guarantee). ), and empathy for the level of satisfaction of commercial customers at PT. Perusahaan Gas Negara (Persero) SBU Distribusi Wilayah III Sumatera Bagian Utara is categorized as sufficient. From the five dimensions of service quality, the most dominant variable inservice improvement is the tangible variable (direct evidence).

Optimal Scheduling of Campus Microgrid Considering the Electric Vehicle Integration in Smart Grid.

High energy consumption, rising environmental concerns and depleting fossil fuels demand an increase in clean energy production. The enhanced resiliency, efficiency and reliability offered by microgrids with distributed energy resources (DERs) have shown to be a promising alternative to the conventional grid system. Large-sized commercial customers like institutional complexes have put significant efforts to promote sustainability by establishing renewable energy systems at university campuses. This paper proposes the integration of a photovoltaic (PV) system, energy storage system (ESS) and electric vehicles (EV) at a University campus. An optimal energy management system (EMS) is proposed to optimally dispatch the energy from available energy resources. The problem is mapped in a Linear optimization problem and simulations are carried out in MATLAB. Simulation results showed that the proposed EMS ensures the continuous power supply and decreases the energy consumption cost by nearly 45%. The impact of EV as a storage tool is also observed. EVs acting as a source of energy reduced the energy cost by 45.58% and as a load by 19.33%. The impact on the cost for continuous power supply in case of a power outage is also analyzed.

Why have U.S. electricity sales flattened?

Since the 2007–2009 financial crisis, U.S. electricity sales have remained essentially flat despite economic recovery, with all but five states registering declines in electricity sales growth between the two decades on either side of the financial crisis. This paper examines the factors that contributed to the decline in annual average electricity sales growth from 1997 to 2007 to 2009–2019 across states. It finds that, at a national level, most (∼60%) of the decline in sales growth was in the commercial sector. The factors driving declines in sales growth across states were heterogeneous. The only statistically significant factor (p < 0.05) was a decline in the growth of the number of commercial customers, which occurred in all but two states. Over the last decade, customer count growth slowed across both commercial and residential classes in almost all states. These changes may reflect structural economic and demographic changes and require further analysis. The results highlight the structural economic factors — as opposed to energy efficiency and other technological factors — that are currently shaping electricity demand in the United States. They underscore the importance, for electric utilities, system operators, and their regulators, of developing more effective strategies to manage load forecast uncertainty.

Real-Time Load Variability Control Using Energy Storage System for Demand-Side Management in South Korea

In today’s power systems, the widespread adoption of smart grid applications requires sophisticated control of load variability for effective demand-side management (DSM). Conventional Energy Storage System (ESS)-based DSM methods in South Korea are limited to real-time variability control owing to difficulties with model development using customers’ load profiles from sampling with higher temporal resolution. Herein, this study thus proposes a method of controlling the variability of customers’ load profiles for real-time DSM using customer-installed ESSs. To optimize the reserved capacity for the proposed maximum demand control within ESSs, this study also proposes a hybrid method of load generation, which synthesizes approaches based on Markov Transition Matrix (MTM) and Artificial Neuron Network (ANN) to estimate load variations every 15 min and, in turn reserve capacity in ESSs. The proposed ESS-based DSM strategy primarily reserves capacity in ESSs based on estimated variation in load, and performs real-time maximum demand control with the reserved capacity during scheduled peak shaving operations. To validate the proposed methods, this study used load profiles accumulated from industrial and general (i.e., commercial) customers under the time-of-use (TOU) rate. Simulation verified the improved performance of the proposed ESS-based DSM method for all customers, and results of Kolmogorov-Smirnov (K–S) testing indicate advances in the proposed hybrid estimation beyond the stand-alone estimation using the MTM- or ANN-based approach.

Customer Switching Intentions in Commercial Banks: A case of selected commercial banks in Dar es Salaam

The negative consequences of customer switching behaviour in the banking industry cannot be underestimated. Since there are limited studies addressing customer switching intentions and actual switching behaviour in the banking sector of Tanzania, this study intended to bridge the knowledge gap by addressing factors influencing customer switching intentions in the context of Tanzanian commercial banks. The study followed a positivism research paradigm out of which a deductive research approach was adopted. In the same line, an explanatory research design was used. The study was conducted in the city of Dar es Salaam with 400 respondents who were customers from NMB Bank Plc, TPB Bank Plc and NBC Bank Ltd. Multi-stage sampling method was used to generate sample size for this study. Multiple linear regression was employed to analyze the collected data. The findings of Thé study indicated that subjective norms were insignificant in influencing customer switching intentions in commercial banks. These findings suggested that commercial banks customers in the city of Dar es Salaam are not under robust social pressure that requires a vital social group to approve their switching intentions. Further findings revealed that perceived control of bank charges, attitude toward service quality and convenience significantly influenced customer switching intentions in commercial banks.

The impact of public policy marketing, institutional narratives and discourses on renewable energy consumption in a developing economy

PurposeThe transition from fossil fuels to renewable energy such as solar energy is difficult and requires significant ongoing public policy marketing initiatives. Drawing on institutional theory, this paper aims to explore how public policy marketing initiatives through institutional narratives and discourses legitimize solar energy's sustainable consumption in a developing economy.Design/methodology/approachUsing a post-structuralist approach, the authors undertook a thematic analysis to study the process of sustainable consumption. The authors conducted face-to-face interviews with key stakeholders in the solar energy sector and complemented the primary data with secondary analysis of archived published materials and podcasts.FindingsFirst, narratives on conformance rules and regulations (regulatory legitimacy) are significant sustainable consumption predictors of solar energy. However, the top-down regulatory legitimation narrative alone is insufficient to overcome poorly developed taken-for-granted (cognitive legitimacy) and morally correct consumption behavior (normative legitimacy), especially among the general population. Second, while consumption is primarily seen as a micro-level, residential and commercial customers phenomenon, the intersecting macro- (government) and meso-levels' (industry/market) narratives and discourses influence and direct micro-level consumption.Originality/valueFuture research agenda on legitimizing the sustainable consumption of solar energy needs to consider the dynamic interactions of institutional narratives and discourses through the lens of institutional theory and practice. Sustained, bold and provident government interventions and actions through market structure and policy issues play a crucial role in the consumption process, particularly in developing economies.