Tariff Plans Research Articles

Measuring Household Thermal Discomfort Time: A Japanese Case Study

This study proposes a metric to measure households’ discomfort related to thermal consumption time (hereafter referred to as t-discomfort). This metric relies on an ideal thermal consumption and calculates the gap between the usage times of thermal devices in vulnerable households compared to the ideal household. The t-discomfort is quantified using thermal data collected from 1298 households in the Tokyo and Oita prefectures in Japan. To create the ideal usage times of thermal devices, households are categorized into three clusters—Vulnerable (Vu), Semi-vulnerable (SVu), and Invulnerable (IVu)—based on their energy poverty ratio, and t-discomfort is subsequently calculated for each group. The IVu households are used as the ideal reference point for measuring thermal device usage in the other two categories. The findings of the study indicate that energy poverty does not necessarily lead to t-discomfort. Interestingly, the consumption time of heating devices among Vu households in both prefectures is longer than that of IVu households, despite the high energy prices. Conversely, SVu households, which do not experience severe energy poverty, tend to sacrifice their comfort by reducing their thermal consumption time. Additionally, the consumption time of cooling devices among Vu households in Oita is longer than that of IVu households, whereas in Tokyo, it is shorter. Two treatment strategies are evaluated to mitigate thermal discomfort in households without compromising resource availability. The first strategy integrates the thermal device consumption time with Japan’s current regulated time-of-use rates plan (daytime and nighttime). The results propose a three-tiered tariff plan (off-peak, mid-peak, and peak) to reduce the energy cost burden for Vu households. The second strategy recommends the installation of 12 rooftop solar panels for households in Tokyo and 11 panels for households in Oita. This strategy aims to maintain thermal comfort via a sustainable natural energy resource while minimizing energy costs.

Empowering E-mobility: Day-ahead dynamic time of use tariff for electric vehicle charging

The greenhouse gases and preservation of the environment have been the most prioritized concern in many countries and international organizations, leading to set aims and initiatives for lowering the dependency on fossil fuels and minimizing carbon emissions. As a part of many initiatives, great emphasis is given to promoting electric vehicles (EVs) over their traditional carbon fuel-based counterpart. To support large-scale EV adoption, a dynamic tariff scheme is required that addresses complex issues like user comfort, savings, and utility revenue. In this research work, a dynamic pricing scheme is modeled for the centralized EV charging stations (EVCS) on a residential feeder. This pricing scheme integrates the existing static time of use (ToU) pricing to an hourly dynamic ToU tariff that varies according to the demand level in the residential feeder. The proposed tariff plan provides a day-ahead dynamic structure with a probable price variation range to aid the EV owners and the day-ahead charging mechanism to schedule the EVs beforehand. The developed scheme calculates the returns and adjusts the tariff including the demand charge and the recovery component on a daily, monthly, and yearly basis for the unsatisfactory investment revenue. Also, user convenience is ensured by setting the minimum possible tariff as well as satisfying the minimum revenue target with the integration of particle swarm optimization (PSO) in designing the pricing structure. The simulation results demonstrate the effectiveness of the proposed dynamic pricing scheme maintaining a trade-off between the interests of all parties involved. Moreover, the simulation outcome of the test case indicates that EV owners can save more than 32 % on charging bills if they plan correctly. On the other hand, the utility can earn more than 2.4 times the existing rate during the peak time of the residential feeder.

LightGBM-, SHAP-, and Correlation-Matrix-Heatmap-Based Approaches for Analyzing Household Energy Data: Towards Electricity Self-Sufficient Houses

The rapidly growing global energy demand, environmental concerns, and the urgent need to reduce carbon footprints have made sustainable household energy consumption a critical priority. This study aims to analyze household energy data to predict the electricity self-sufficiency rate of households and extract meaningful insights that can enhance it. For this purpose, we use LightGBM (Light Gradient Boosting Machine)-, SHAP (SHapley Additive exPlanations)-, and correlation-heatmap-based approaches to analyze 12 months of energy and questionnaire survey data collected from over 200 smart houses in Kitakyushu, Japan. First, we use LightGBM to predict the ESSR of households and identify the key features that impact the prediction model. By using LightGBM, we demonstrated that the key features are the housing type, average monthly electricity bill, presence of floor heating system, average monthly gas bill, electricity tariff plan, electrical capacity, number of TVs, cooking equipment used, number of washing and drying machines, and the frequency of viewing home energy management systems (HEMSs). Furthermore, we adopted the LightGBM classifier with ℓ1 regularization to extract the most significant features and established a statistical correlation between these features and the electricity self-sufficiency rate. This LightGBM-based model can also predict the electricity self-sufficiency rate of households that did not participate in the questionnaire survey. The LightGBM-based model offers a global view of feature importance but lacks detailed explanations for individual predictions. For this purpose, we used SHAP analysis to identify the impact-wise order of key features that influence the electricity self-sufficiency rate (ESSR) and evaluated the contribution of each feature to the model’s predictions. A heatmap is also used to analyze the correlation among household variables and the ESSR. To evaluate the performance of the classification model, we used a confusion matrix showing a good F1 score of 0.90. The findings discussed in this article offer valuable insights for energy policymakers to achieve the objective of developing energy-self-sufficient houses.

Day ahead demand response model with algorithm-based consumption classification and tariff planning

Demand Response (DR) programs effectively support the supply-demand balance in power systems. This study proposes a new Time-of-Use (ToU) tariff model, a type of price-based demand response (DR). The proposed model aims to shape the daily consumption profile of the distribution region by encouraging consumers through tariffs. Generally, in ToU demand response, various presumptions are being adopted to define consumption periods and consumer classes. It is unlikely that these presumptions are suitable for every day of the year and every consumer in the system. Achieving these definitions and classifications based on algorithms, free from human error, will increase the success and comprehensiveness of the DR program. To maximize success in this context, a deep learning model has been leveraged for day-ahead consumption forecasting. The forecasted daily consumption profile is partitioned into ToUs by using the Moving Boundary method. In addition, consumers within the region have been segmented based on the similarities in consumption behavior. Tariffs have been priced for each cluster through an optimization model that considers their participation rates in total consumption and contributions to consumption in ToU regions. The proposed model has been tested over a dataset belonging to a distribution system where different types of consumers coexist. Two sample days have been chosen as one is a weekday and the other is a weekend. The results prove that shifting consumption as encouraged by the proposed TOU method reduces consumers' costs. While, the profitability of the system operator is also preserved.

The Efficacy of Autoencoders in the Utilization of Tabular Data for Classification Tasks

This article explores the effectiveness of applying auto encoders in the context of classical machine learning tasks, particularly classification tasks on tabular data. Significant emphasis is placed on a detailed comparison of this approach with other widely adopted methods in the field. As an applied task, the creation of a look-alike model for targeted advertising is pursued. Research into the requirements for this model reveals that conventional approaches to processing tabular data and model training cannot yield a solution that qualitatively meets all specified criteria. Evaluation is conducted on a closed dataset provided by a telecommunications operator. This de-identified dataset includes subscriber descriptions, their tariff plans, and mobile devices. The research goal is to assess non-traditional approaches from the domain of deep learning and neural networks in the context of classification tasks on tabular data and compare them with established methods in this context. The research findings demonstrate that the investigated approach yields a solution that surpasses traditional approaches across all specified criteria. Furthermore, the study provides insights into the universality of this approach, indicating its applicability in various domains for tabular data classification tasks. The ability to compress information into vector representations without significant loss and identify new hidden nonlinear dependencies in data enhances the practical value of the approach. Future research will address the formation of multiple autoencoders in different knowledge domains and their integration for specific tasks. Additionally, efforts will focus on identifying the most effective algorithm for finding similar subscribers based on the vector representations obtained from autoencoders.

Mechanism for Improving the Competitiveness of Brokerage Organizations in the Brokerage Services Market

The article concretizes the problems that arise among professional participants of the securities market in the implementation of brokerage activities and hinder the development of competition in the market of brokerage services. Taking into account the current trends in the development of competitive relations in the brokerage services market, the methods of solving the identified problems in the algorithm «trend — problem — potential solution» through the prism of a systemic vision are proposed. Various directions for improving the competitiveness of brokerage organizations are formulated, in particular: development of financial engineering; creation of premium tariff plans; improvement of digital client services; synergy of various types of professional activities in the stock market; implementation of the technology of robo-advising etc. The list of resources required to implement the proposed directions for improving the competitiveness of brokerage organizations has been specified. A mechanism for improving the competitiveness of brokerage organizations has been proposed. A detailed description and justification of the components of the mechanism is given. Using the example of the BC «REGION», which is part of the competitive environment of leaders, identified by the results of previous studies, the work of the mechanism for improving the competitiveness of brokerage organizations has been demonstrated.

Changing Electricity Tariff—An Empirical Analysis Based on Commercial Customers’ Data from Poland

Nearly 60% of commercial customers are connected to a low-voltage network in Poland with a contractual capacity of more than 40 kW and are assigned a fixed tariff with flat prices for the whole year, no matter the usage volume. With smart meters, more data about how businesses use energy are becoming available to both energy providers and customers. This enables innovation in the structure and type of tariffs on offer in the energy market. Customers can explore their usage patterns to choose the most suitable tariff to benefit from lower prices and thus generate savings. In this paper, we analyzed whether customers’ electricity usage matched their optimal tariff and further investigated which of them could benefit or lose from switching the tariff based on the real dataset with the hourly energy readings of 1212 commercial entities in Poland recorded between 2016 and 2019. Three modelling approaches, i.e., the k-nearest neighbors, classification tree and random forest, were tested for optimal tariff classification, while for the benchmark, we used a simple approach, in which the tariff was proposed based on the customers’ previous electricity usage. The main findings from the research are threefold: (1) out of all the analyzed entities, on average, 76% of them could have benefited from the tariff switching, which suggests that customers may not be aware of the tariff change benefits, or they had chosen a tariff plan that was not tailored to them; (2) a random forest model offers a viable approach to accurate tariff classification; (3) the policy implication from the research is the need to increase the customers’ awareness about the tariffs and propose reliable tools for selecting the optimal tariff.

Дослідження програмних засобів прогнозування вироблення та споживання електроенергії в Україні на основі методів машинного навчання

The problem of security in energy sector is an important aspect for Ukraine. The purpose of monitoring in this area is to optimize the flow of electricity between market participants, between European partners and Ukraine. It is critically important to maintain a balance between producers and consumers of energy. Both over and undersupply of energy represent risks to infrastructure. The previously available wholesale electricity market model with single buyer has been replaced by a model based on bilateral, day-ahead and intraday markets, as well as balancing and ancillary services markets. Now the participants can freely trade electricity and energy companies can provide services that provide stability of the energy system and supply electricity to the final consumer. The demand forecasting in electricity markets is one of the components that must be implemented for successful business operations and optimization of business processes. Based on the model of the Institute of problems of modeling electricity engineering of NANU, the paper sets out the task of developing a software system for forecasting threats in the energy sector of Ukraine using machine learning methods. Experiments were conducted on the application of regression methods to restore a column with data from bilateral contracts for the task of forecasting electricity generation and consumption. The results of the application of machine learning algorithms on peacetime data demonstrated that it is possible to predict market volumes and tariff plans one hour in advance with a good accuracy which allows to go beyond one-day planning in the future.

Is having an expert “friend” enough? An analysis of consumer switching behavior in mobile telephony

We present novel evidence from a large panel of UK consumers who receive personalized reminders from a specialist price-comparison website about the precise amount they could save by switching to their best-suited alternative mobile telephony plan. We document three phenomena. First, even self-registered consumers with positive savings exhibit inertia. Second, we show that being informed about potential savings has a positive and significant effect on switching. Third, controlling for savings, the effect of incurring overage payments is significant and similar in magnitude to the effect of savings: paying an amount that exceeds the recurrent monthly fee weighs more on the switching decision than being informed that one can save that same amount by switching to a less inclusive plan. We interpret this asymmetric reaction on switching behavior as potential evidence of loss aversion. In other words, when facing complex and recurrent tariff plan choices, consumers care about savings but also seem to be willing to pay upfront fees in order to get “peace of mind”.

Key Development Trends Competition in the Product Segments of the Brokerage Services Market of the Russian Federation

The development of competition between professional participants in the securities market engaged in brokerage activities is inseparably linked with the growth of the competitiveness of brokerage organizations in the framework of improving the quality of the services and products they provide. In this regard, improving the competitiveness of brokerage organizations is an indispensable condition for developing competition in the brokerage market, and hence increasing the investment attractiveness of the securities market of the Russian Federation. The article examines the activities of brokerage organizations in the Russian securities market and determines the dynamics of key indicators characterizing brokerage activities (the number and structure of active clients and the volume and structure of client transactions). A comparative analysis of the activities of leading brokers by product segment, tariff plans and combined types of activities was carried out, as a result of which key trends in the development of brokerage activities were identified. Based on the identified trends, an assessment of the competitive advantages of leading brokers was carried out and companies-leaders and companies included in the competitive environment of leaders that need to increase their competitiveness were identified.

Determination of the responsibility of delay in the context of airline business model: A case of American Airlines and Southwest Airlines

The main factor in the airline transport service being the reason for the choice is the place and time benefit it provides. Delays are negative situations that disrupt the time utility, reduce airlines' profits, cause congestion trouble and disrupt tariff plans. The first part of the study consists of information about delays and fundamental issues related to delays. In the second part, it has been tried to summarize the studies on the slot, which is directly related to the delays. In the last part, two airlines that adopted different business models, full-service carrier and low-cost carrier, were compared based on delay reasons. The study aims to determine the causes of delay and their predictive roles comparatively. We have used the multiple hierarchical regression model for this purpose. American Airlines and Southwest Airlines were selected as full-service carrier and low-cost carrier, respectively. We have determined that even though Southwest Airlines is a low-cost carrier, and more punctual than American Airlines, delays stemming from the carrier play a greater role in overall delays than American Airlines.

Public preferences and increasing acceptance of time-varying electricity pricing for demand side management in South Korea

Linking demand and supply management on the basis of public acceptance is essential to prepare for the energy transition from fossil fuels to environment friendly energy integration, and to ensure stability in electricity supply and demand management. Demand management could promote the conservation of energy resources by stabilizing electricity supplies and curbing consumer consumption, which could consequently help reduce capital expenditures. Major developed countries have improved the efficiency of electric facility operations by implementing demand management based on pricing programs. The Korean government also seeks to improve demand management by introducing time-of-use (TOU) tariffs to residential electricity consumers. However, the rate of residential customers switching to the TOU tariff in Korea is still low, which negatively affects the success of demand management. Accordingly, this study aims to consider the preference structure of residential electricity consumers for TOU pricing using a discrete choice experiment, and further derive policy implications to promote the pricing program's switching rate. The analysis of consumer preferences in this study shows that consumers prefer a TOU tariff plan that minimizes uncertainty. Therefore, it is necessary to provide consumers with information on their consumption amounts and usage patterns based on real-time metering to reduce consumer uncertainties. In conjunction with the findings from the scenario analysis on the choice probability of TOU tariffs, incentives such as subsidies for purchasing high-efficiency appliances and carbon points are an effective measure to lower the barrier to switching, and reduce additional utility expenses. Furthermore, diversifying tariff plan attributes is required in subsequent studies on public acceptance to accurately identify consumer preferences while considering the TOU tariff plan's rate of diffusion.

Pricing on‐demand services: Alternative ways of combining usage and access fees

Many firms are adopting an “everything or anything” as a service—“XaaS”—model to sell goods and value‐added services. Buyers for such goods are heterogeneous both in the number of units they desire and the valuations for each unit. Suppliers designing such revenue models can offer either a usage‐based per‐unit fee or an access‐based per‐period fee, often combining them both as a nonlinear two‐part tariff plan that imposes both types of fees on all buyers or let buyers self‐select from a menu of per‐unit and per‐period plans. We develop a theoretical model to analyze the economic implications of alternative designs for mixing per‐unit and per‐period fees. Our analysis produces the following practical insights on these plans' profitability and market coverage. First, the menu is generally a better way of combining access and usage fees for a firm selling digital goods with zero variable costs. Second, the preferred design switches to a two‐part tariff if the firm's production environment resells or runs on top of a back‐end infrastructure or data service provider that imposes nonnegligible variable costs. Third, we show that the revenue advantage of these two designs (which employ both fees) over a simple plan that employs only one fee (best of per unit and per period) is most significant when the rate of change in marginal valuation is relatively similar across buyers.

Correction: Mekonnen et al. Modeling, Analysis and Optimization of Grid-Integrated and Islanded Solar PV Systems for the Ethiopian Residential Sector: Considering an Emerging Utility Tariff Plan for 2021 and Beyond. Energies 2021, 14, 3360

The authors wish to make the following corrections to their paper [...]

Technical Measures for Prevention of Theft Committed with the Use of Information and Telecommunication Technologies

Theft committed with the use of information and telecommunication technologies has taken a lead in cybercrimes and is a major concern of developing countries. Provision of effective prevention activity in the sphere under study poses a serious problem. Its solution depends on a systematic approach to resolving organizational and legal aspects of social prevention of this type of theft. The specificity of IT-theft (stealing and fraud) calls for correction of established methods of special prevention measures and building new approaches to such activity (technologisation of the process). The article allocates a set of major perspective technical measures for prevention of IT-theft aimed to develop and implement a single monitoring system (data mining and extraction of useful data from total data volumes); the fight against anonymity (the possibility of de-anonymization of users is considered). The study also highlights countering fraudulent spam (the possibility of using the technology of «gray lists», the introduction of a specialized tariff plan when using e-mail as well as the development of domestic software based on individual Bayesian filters capable of analyzing the statistics of the occurrence of words in the texts of fraudulent messages). The main directions of practical implementation of the proposed approaches are determined. In conclusion, the author emphasizes the fact that the application of these technical preventive measures will allow the entire cybercrime prevention system to be holistic and logically complete.

Socio-economic Impact of Rural Road Infrastructure by African Development Bank Community Based Agriculture and Rural Development Project in Selected States of Nigeria

The study was carried out to assess the socio-economic impact of rural road infrastructure provided by the African Development Bank Community Based Agriculture and Rural Development Project (AfDB-CBARDP) in the States of Adamawa, Bauchi, Gombe, Kaduna and Kwara. Multi-stage sampling procedure was used to select samples for the study. A sample of 80 respondents were used for the study. The data was analyzed using descriptive statistics. The results revealed that the project had achieved 100% set target on the provision of road construction in each of the participating States. Also, 83.0%, 76.0%, 69.0%, 79.0% and 72.0% of beneficiaries’ communities in Adamawa, Bauchi, Gombe, Kaduna and Kwara States rated the roads quality as very good. The result has shown that in Adamawa, Bauchi, Gombe, Kaduna and Kwara States, there was reduction in time of waiting for motor vehicle by 160, 15, 50, 40 and 10 minutes. There was also a concurrent reduction in the transportation fare (Naira/passenger) to the nearby town by ₦50, ₦200, ₦100, ₦150 and ₦50, respectively. The study equally revealed that average yield per hectare of maize has increased from 1500 kg to 2500 kg before and after the project in Adamawa State. In the same vein, average yield in Bauchi, Gombe, Kaduna and Kwara States before the project were 1100 kg, 1200 kg, 1745.6 kg and 1223 kg, while after the project intervention the maize yields more than double to 2500 kg, 2800 kg, 2309 kg and 2309 kg, respectively. Likewise, average yield of cowpea per hectare has increased considerably across the five States of Adamawa, Bauchi, Gombe, Kaduna and Kwara by 69.8%, 65.9%, 51.6%, 73.8% and 46.5%, respectively. Percentage change in income of the sampled respondents indicates an average increase of 67.6% across the beneficiary States. The study concluded a strong positive NPVs and BCRs greater than 1 in all the States. The BCR in all cases indicates that the road projects were worth investing. The study recommended that a willingness to pay analysis to be undertaken as a way of gauging future tariff plan for the use of the road infrastructure to justify and attract private investment.

Choosing a web hosting service for applications based on Django framework

Problem. The article deals with the issues of choosing a hosting provider for Django-based web applications. It is noted that the use of Python for web development is due to its simplicity, flexibility, and scalability, the possibility of rapid prototyping of web applications, the availability of standard libraries to solve a wide range of problems, and a large community of developers. It is indicated that almost all server-side applications are developed using one or another web framework. Among server-side web frameworks based on Python programming language, Django is often considered one of the best. This framework is used on many wellknown websites. Goal. The goal is to choose a web hosting service to deploy a Django-based web application. Methodology. The article describes the performance metrics of applications deployed on any web hosting service. We consider the steps to choose a web hosting service using KnAS4Web application as an example. Results. A comparative analysis of free plans for Heroku and PythonAnywhere web hosting services is performed. From the given data it is concluded that the reasonable choice is to use PythonAnywhere service for hosting web application KnAS4Web. To ensure minimal latency, it makes sense to deploy it on the servers located in the European region, namely, at the data center in Frankfurt, Germany. Originality. For productionready web applications such as KnAS4Web, it is suggested to consider only web hosting services with free tariff plans available. To compare the services, we analyze their disk space available, annual uptime, the maximum amount of traffic per month, data centers location, and ability to provide HTTPS connection as well as operative help. Practical value. The approach allows to significantly limit the range of potential web hosting services and helps to choose the service for deploying a web application.

An empirical model of the effects of “bill shock” regulation in mobile telecommunication markets

We develop an empirical model of consumer usage and price uncertainty under a three-part tariff plan to study the effects of an enacted “bill shock” agreement in mobile telecommunication markets, which requires mobile network operators to inform consumers when they use up the monthly allowance of their mobile phone plan. Using a rich billing dataset, we estimate an industry model of calling, subscription, and pricing. Our counterfactual simulations, which incorporate operators’ price responses to compute the new equilibrium, show that the regulation would lead to lower fixed fees, lower allowances, and lower overage prices chosen by the operators. The regulation and the pricing changes in response to it would benefit both the consumers and the operators, and overall the total surplus would increase by $307 million per month. An increase in mobile penetration explains the joint increase in firm profits and consumer surplus.

Empirical risk assessment of maintenance costs under full-service contracts

We provide a data-driven framework to conduct a risk assessment, including data pre-processing, exploration, and statistical modeling, on a portfolio of full-service maintenance contracts. These contracts cover all maintenance-related costs for a fixed, upfront fee during a predetermined horizon. Charging each contract a price proportional to its risk prevents adverse selection by incentivizing low risk (i.e., maintenance-light) profiles to not renege on their agreements. We borrow techniques from non-life insurance pricing and tailor them to the setting of maintenance contracts to assess the risk and estimate the expected maintenance costs under a full-service contract. We apply the framework on a portfolio of about 5000 full-service contracts of industrial equipment and show how a data-driven analysis based on contract and machine characteristics, or risk factors, supports a differentiated, risk-based break-even tariff plan. We employ generalized additive models (GAMs) to predict the risk factors’ impact on the frequency (number of) and severity (cost) of maintenance interventions. GAMs are interpretable yet flexible statistical models that capture the effect of both continuous and categorical risk factors. Our predictive models quantify the impact of the contract and machine type, service history, and machine running hours on the contract cost. We additionally utilize the predictive cost distributions of our models to augment the break-even price with the appropriate risk margins to further protect against the inherently stochastic nature of the maintenance costs. The framework shows how maintenance intervention data can set up a differentiated tariff plan.

A Machine Learning Model for Personalized Tariff Plan based on Customer’s Behavior in the Telecom Industry

In the telecommunication industry, being able to predict customers’ behavioral pattern to successfully design and recommend a suitable tariff plan is the ultimate target. The behavioral pattern has a vital connection with the customers’ demographic background. Different researches have been done based on hypothesis testing, regression analysis, and conjoint analysis to determine the interdependencies among them and the effects on the customers’ behavioral needs. This has presented us with ample scope for research using numerous classification-based techniques. This work proposes a model to predict customer’s behavioral pattern by using their demographic data. This model was built after investigating various types of classification-based machine learning techniques including the traditional ones like decision tree, k-nearest neighbor, logistic regression, and artificial neural networks along with some ensemble techniques such as random forest, adaboost, gradient boosting machine, extreme gradient boosting, bagging, and stacking. They are applied to a dataset collected using a questionnaire in India. Among the traditional classifiers, decision tree gave the best result of 81% accuracy and random forest showed the best result among the ensemble learning techniques with an accuracy of 83%. The proposed model has shown a very positive outcome in predicting the customers’ behavioral pattern.