Manage Electricity Consumption Research Articles

Quantum Informative Analysis in Smart Power Distribution

Advancements in the Internet of Things (IoT) paradigm have greatly improved the quality of services in the electricity industry through the integration of smart energy distribution and dependable electric devices. Conspicuously, the current research introduces a method for managing electricity consumption in smart residences using IoT-Fog technology, focusing on efficient energy allocation and real-time energy needs. The study specifically examines the effectiveness of electricity grid sub-stations in distributing energy using fog computing technology. By utilizing a Quantum Computing-assisted approach, optimal energy distribution is achieved by calculating a novel Electricity Usage Measure (EUM) based on actual energy usage patterns of smart homes. Furthermore, the Quantumized Neural Network (QiM-NN) technique is developed to forecast the electricity distribution over grid substations. For performance assessment, 4-month data is collected using four smart houses. Comparative analysis with existing data assessment techniques illustrates the effectiveness in terms of Temporal Delay (6.33 ms), Optimization Performance (Specificity (93.00%), Sensitivity (90.86%), Precision (96.66%), Coverage (96.66 %)), Reliability (93.76%), and Stability (71%).

Model checking programs in process-oriented IEC 61131-3 Structured Text

The process-oriented programming is a paradigm based on the process concept where each process is a concurrent finite state machine inside. The paradigm is intended for PLC (programmable logic controllers) developers to write Industry 4.0-enabled software. The poST language is a promising process-oriented extension of the IEC 61131-3 Structured Text (ST) language designed to provide a conceptual consistency of the PLC source code with technological description of the process under control. This language combines the advantages of FSM-based programming with the standard syntax of the ST language. We propose transformational semantics of poST providing rules for translation of poST language statements to Promela — the input language of the SPIN model checker. Following these semantic rules, our Xtext-based translator outputs a Promela model for the poST program. Our contribution is the poST transformational semantics and the method for automatic generation of the Promela code from poST control programs. The resulting Promela program is ready to be verified with SPIN model checker against linear temporal logic requirements to the source poST program. In the paper we provide an overview of related work, as well as a brief description of the poST and Promela languages. Further, the Promela poST translation rules cover control flow statements, process creation and state management constructs, and timeout management. Then we define service processes for modeling the external environment and managing high-level LTL specifications. Then we present the main ideas of implementing the translator poST to Promela. We also illustrate our approach using the example of a system for managing electricity consumption and production, including renewable sources.

Managing Electricity Consumption on Campus: The Effect of Online Learning from Home

This paper analyzes the effect of Online Learning from Home (OLFH) during the COVID-19 pandemic on electricity consumption and the quality of the learning process quality. This study identifies the reduction of electricity consumption on campus during OLFH, which can be utilized to maintain the quality of the learning process. The electricity consumption data was analyzed from the direct measurement using the electricity consumption real-time monitoring system. Again, the data analyzed of the online learning process quality was obtained from the student survey via Google Forms. The results of monitoring electricity consumption before and during OLFH showed that the most significant decrease in electricity consumption occurred in classroom buildings, with a decrease value of 68.74%. Meanwhile, the lowest reduction of electricity consumption occurred in the rectorate building, with its reduced value of 19.02%. A student survey showed that most respondents were satisfied with the transition from face-to-face learning to OLFH during the COVID-19 pandemic. However, this study also identified negative aspects of OLFH. Reducing electricity consumption during OLFH will cost saving for the university. The cost saving can be used to implement programs to address the deficiencies of OLFH and improve its infrastructure so that the quality of the learning process is maintained.

ВИКОРИСТАННЯ ШТУЧНИХ НЕЙРОННИХ МЕРЕЖ ДЛЯ ПРОГНОЗУВАННЯ ВИРОБЛЕННЯ СОНЯЧНОЇ ЕНЕРГІЇ НА ФОТОЕЛЕКТРИЧНИХ ЕЛЕМЕНТАХ

Purpose. Development of an artificial neural network for modeling time series of forecasting electricity generation taking into account the use of a renewable energy source using MATLAB&Simulink software. Methodology. Theoretical and experimental research with the help of software implementation of fundamental knowledge in the field of electricity generation using renewable energy sources; simulation of artificial neural network prediction; methodology of mathematical statistics and forecast analysis. Results. Based on the conducted research and implemented solutions, this article developed a time series modeling system for forecasting solar energy production using Simulink. Defined control parameters and corresponding time series; the analysis of the input data showed the influence on the generated electrical energy of such meteorological parameters as solar radiation on the surface of the earth and on the surface of the panel, temperature of the panel and the environment, cloud cover and time of day. In order to ensure the appropriate accuracy of forecasting the generated power, there was a task to ensure the accuracy limit of forecasting meteorological parameters. Scientific novelty. The novelty of the given research lies in the formation of the balance of electricity in the power system with the help of an artificial neural network, influencing the choice of mode parameters and calculated loads. The balance between production and consumption of electricity is a fundamental aspect of the technological sustainability of the energy system. Violation of this balance can lead to deterioration of power quality, including changes in frequency and voltage in electrical networks, which can affect the efficiency of equipment. Practical significance. Neural network forecasting allows effectively distribute the load between different objects of the power system. This makes it possible to manage electricity consumption by adjusting the load of equipment. For example, the main electricity generation can be shifted to hours and zones of the wholesale electricity market with lower prices to reduce the cost of electricity generated from renewable energy sources.

Possibilities of managing electricity consumption through the application of time-classified tariffs

This manuscript is devoted to the issue of smoothing the load schedule of enterprises in “peak” periods in return for using the results of energy audits and applying a system of time-classified tariffs. Considering the mentioned issue above, using the results of the energy inspection and using the system of time-classified tariffs will create an opportunity to achieve smoothing the load schedule of the electric power system during “peak” periods. The use of the time-classified tariff system by consumers will reduce the loading period of the electric power system and change its regimes.

Control mechanism of electricity consumption in a transport company

To reduce electricity consumption in hierarchical management of fabrication, it is necessary to take into account the activity of managers associated with the presence of their own targets. A three-level model of managing electricity consumption in production is considered. At its top level is the superior who evaluates the executive who is at the middle level. At the bottom of the system is the director of fabrication. The superior must to manage the consumption of the executive in such a way as to save electricity. But the executive knows own electricity-saving opportunities better than the superior. In turn, the director knows his own electricity-saving capabilities better than the executive. So, both the executive and the director can manipulate their electricity consumption to gain more incentives. To avoid this, a control system for electricity consumption management in fabrication is proposed. This system includes procedure for machine learning of the superior and procedure for grading of the executive. Sufficient conditions of the optimality of this system are found, in which random opportunities of decreasing electricity consumption are used. With such a system, the executive is interested in minimizing electricity consumption. The Theorem is proved that for this it is sufficient to use linear procedures of adaptive standardization and stimulation of the director. The proposed approach to control of electricity consumption is illustrated by the example of railcars repairing in the Russian Railways company.

Designing an energy management system for household consumptions with an off-grid hybrid power system

<abstract> <p>This paper analyzes the effect of meteorological variables such as solar irradiance and ambient temperature in addition to cultural factors such as consumer behavior levels on energy consumption in buildings. Reducing demand peaks to achieve a stable daily load and hence lowering electricity bills is the goal of this work. Renewable generation sources, including wind and Photovoltaics systems (PV) as well as battery storage are integrated to supply the managed home load. The simulation model was conducted using Matlab R2019b on a personal laptop with an Intel Core i7 with 16 GB memory. The model considered two seasonal scenarios (summer and winter) to account for the variable available energy sources and end-user electric demand which is classified into three demand periods, peak-demand, mid-demand, and low-demand, to evaluate the modeled supply-demand management strategy. The obtained results showed that the surrounding temperature and the number of family members significantly impact the rate of electricity consumption. The study was designed to optimize and manage electricity consumption in a building fed by a standalone hybrid energy system.</p> </abstract>

Economics of Electricity Consumption: A Literature Review

Electricity consumption is determined by the use of electronic gadgets, income level of family members, choice of connection and size of the houses. In India, the energy use is at disaggregate level. Electricity is necessary in various types of activities and in each it takes a different character. There is a strong two-way relationship between economic development and energy consumption. The dynamics of growth in one sector with more residential electricity brings realistic potential for efficiency. The increasing availability and usefulness of electricity set the limit to its requirements in a region and the actual level of its consumption at any given time depends on a number of socio-economic factors which are usually not taken into account in making estimates of future requirements. In India, the growth in demand has exceeded the growth in generation capacity as a result the country is facing acute energy and peak shortages. The studies also discuss possible pricing options to bring about more efficient use of limited generated capacity as well as addressing the issue of equity. Some studies also tried to estimate the final demand which is price sensitive for both residential and industrial consumers. This study attempted to bring out literature reviews covering Studies on Electricity Economics; Studies on Household Electricity Consumption; and Studies on Managing Electricity Consumption.

Способ энергосбережения энергетических установок на перспективных кораблях ВМФ

Energy consumption management will allow to increase the energy efficiency of ships mechanisms in the long term, by the short term it will help to develop and implement mechanisms for managing electricity consumption. Scientifically grounded energy consumption can be used as a management tool and improving the quality and efficiency in decision-making. Minimum energy consumption (minimal cost of the consumed energy) can be used as a criterion for the method of a decision-making.

Smart meter data classification using optimized random forest algorithm

Implementing a proper clustering algorithm and a high accuracy classifier for applying on electricity smart meter data is the first stage in analyzing and managing electricity consumption. In this paper, Random Forest (RF) classifier optimized by Artificial Bee Colony (ABC) which is called Artificial Bee Colony-based Random Forest (ABC-RF) is proposed. Also, in order to determine the representative load curves, the Convex Clustering (CC) is used. The solution paths generated by convex clustering show relationships among clusters that were hidden by static methods such as k-means clustering. To validate the proposed method, a case study that includes a real dataset of residential smart meters is implemented. The results evidence that the proposed ABC-RF method provides a higher accuracy if compared to other classification methods.

Research on big data analysis model of multi energy power generation considering pollutant emission—Empirical analysis from Shanxi Province

With the development of the integrated energy Internet, energy structure optimization and emission reduction have led to higher requirements for developing various energy sources to enable coordinated and sustainable development. However, data-mining methods are rarely used to study the coordination of multi-energy generation in published research results. In this study, from the perspective of power industry emissions, coordinated generation of various energy sources, and balance of power generation and consumption, a data-mining algorithm was used to analyze the development of thermal power, hydropower, wind power, waste heat, gas, and other power sources. The chi-square automatic interaction detection tree (CHAID), logistic regression, and two-step clustering methods were applied. The results show that: a) CO2 and SO2 emissions were mainly affected by thermal power generation, whereas NOx emissions were jointly affected by thermal power, garbage power, and gas-fired power, and the emissions of various pollutants increased with an increase in power consumption. The optimal power-generation scheme under minimum emission can be obtained. b) There was a strong correlation between thermal power generation and residential electricity consumption, and renewable energy (wind energy, photovoltaic, hydropower) exhibited the highest correlation with the electricity consumption of the tertiary industry, which indicates that renewable energy generation can be promoted by managing electricity consumption in the tertiary industry. c) When the electricity demand of all users was small, the proportion of renewable energy power generation increased; in contrast, the thermal power generation was larger. This indicates the importance of improving the sustainable and stable power supply of renewable energy. This study provides a data analysis model for the coordinated development of multiple energies, which will contribute to the decision-making basis for controlling power emissions, improving the utilization rate of renewable energy, and optimizing the energy structure.

Managing electricity consumption for household sector in Indonesia

Wydawnictwo SIGMA-NOT wydaje czasopisma fachowe informujące swoich czytelników o najnowszych osiągnięciach naukowych i nowoczesnych rozwiązaniach technicznych w Polsce i na świecie, popularyzuje problemy techniczne oraz poszerza wiedzę i kulturę techniczną.

Fuzzy Control System for Smart Energy Management in Residential Buildings Based on Environmental Data

Modern energy automation solutions and demand response applications rely on load profiles to monitor and manage electricity consumption effectively. The introduction of smart control systems capable of handling additional fuzzy parameters, such as weather data, through machine learning methods, offers valuable insights in an attempt to adjust consumer behavior optimally. Following recent advances in the field of fuzzy control, this study presents the design and implementation of a fuzzy control system that processes environmental data in order to recommend minimum energy consumption values for a residential building. This system follows the forward chaining Mamdani approach and uses decision tree linearization for rule generation. Additionally, a hybrid feature selector is implemented based on XGBoost and decision tree metrics for feature importance. The proposed structure discovers and generates a small set of fuzzy rules that highlights the energy consumption behavior of the building based on time-series data of past operation. The response of the fuzzy system based on sample input data is presented, and the evaluation of its performance shows that the rule base generation is derived with improved accuracy. In addition, an overall smaller set of rules is generated, and the computation is faster compared to the baseline decision tree configuration.

ECONOMETRIC MODELLING OF THE CONSUMPTION OF ELECTRICITY IN GHANA WHEN STRUCTURAL BREAKS EXIST

The research modelled electricity consumption for Ghana using annual data for the period 1971-2011, obtained from world development indicator. The research adopts the Gregory and Hansen model of cointegration for the estimation in the presence of structural breaks. The results reveal stable short run and long-run relationships among the explanatory variables and electricity consumption. The findings suggest that financial development explain electricity consumption in Ghana both in the short run and in the long run. The other variables (trade openness, price, and income) in the estimated model do not significantly explain electricity consumption. Therefore, they are not reliable policy variables in managing electricity consumption.

Smart Energy Meter: Applications, Bibliometric Reviews and Future Research Directions

ABSTRACT Now, Smart Energy Meters (SEMs) have become very popular in controlling and managing electricity consumption in residential, commercial, industrial sectors, to name a few. SEMs collects the fine-grained energy-consumption data at a regular time interval (every hour). Such a low sampling data rate can be useful in research, evaluating the higher consumption of electricity, etc. However, existing research work cannot adequately assess the qualitative, quantitative, and logical properties of the data that could theoretically improve the performance of classification and analytics. The purpose of this bibliometric analysis is to understand the reach of SEMs and its data analysis worldwide and across the various applications to make the concept clear for future researchers. The Scopus, Semantic Scholar and Crossref databases are used for performing the bibliometric analysis. This research study revealed that the insights of the SEMs data-enabled significant advancement in many fields such as energy consumption pattern analysis and prediction, demand response, load profiling, and direct-indirect phone charging analysis.

Simulation Models for Management Solutions when Implementing Innovative Technologies

The object of empirical research is a company that develops, produces and implements innovative patented technology for managing electricity consumption, naturally fitting into the concept of Smart Grid. The author has developed simulation models for predicting the behavior of an innovative technology market. A paradigm of simulation modeling system dynamics is investigated and implemented. In this work, hypotheses about the origin and significant development of communications between market segments are put forward and investigated. The introduction of such links in the developed simulation models is completely natural and technically simple, but their use requires additional research. Mass simulation runs of the developed models with the introduction of new market relations and parameters make it possible to more accurately understand the market behavior mechanisms of the innovative patented technology, use these mechanisms to develop models, build sound long-term forecasts, develop realistic production plans, improve management efficiency through the introduction of the innovative technology.

Household behavior to manage electricity consumption after the tariff increase

The event of price electricity increases in Indonesia since 2017 is expected to encourage households to take planned actions on electricity consumption. Hence, this study is intended to reveal the behavior of households to support electricity consumption saving program of the household consumers. By using the Theory of Planned Behavior approach, it is known that: 1) the respondents realize the importance of saving electricity consumption through their behavior; varied regulating electricity equipment, providing household appliances, the use of electricity saving devices. 2) However, households are hindered by psychological constraints about their uncertainty rewards. 3) There is no evidence that households take planned actions in order to use electricity more economically. Thus, it is recommended that PLN (State Owned Electricity Corporation) continue to carry out integrated communication over household consumer about practices to save electricity. It could be conducted by introducing values established, including rewards that can be adopted by households as part of education to the households.

Application of intelligent load control to manage building loads to support rapid growth of distributed renewable generation

Electricity utilities are faced with the mounting challenge of providing a stable supply of power to meet the growing demand while also integrating rapid growth in distributed variable renewable generation. Traditional means of balancing short- and long-term supply and demand imbalance will be expensive. Alternative approaches of using flexible loads in buildings are needed to mitigate the imbalance at a lower cost. This paper shows how the intelligent load control (ILC) process can be used to manage loads in buildings by dynamically prioritizing loads for curtailment using both quantitative and qualitative criteria. The ILC process can be deployed on low-cost computing platforms without the need for any additional sensing. ILC was first validated in a simulation environment to provide two grid service use cases: (1) managing monthly peak electricity demand and (2) managing buildings’ electricity consumption during a capacity bidding event. After being successfully tested in a simulation environment, ILC was deployed on real buildings to manage electricity consumption to provide two different use cases under different outdoor operating conditions. Both the simulation tests and the real building experiments were deployed using VOLTTRON™, a distributed sensing and control platform. The results from the tests and experiments showed that ILC was able to manage the controllable loads (heat pumps) in the building to maintain the electricity consumption at the desired level without a significant impact on occupant comfort. Overall, the results demonstrate that the ILC allows coordination of the controllable loads and provides a more intelligent means of load management than the traditional duty-cycling approach.

Price setting in the retail electricity market under the Bertrand competition

The paper is concerned with the coordination of interaction between various types of consumers and a power company to manage electricity consumption by using the adverse selection model based on contract theory. There are several power companies competing in the electricity market according to the Bertrand model. A method is proposed for load curve optimization by different types of consumers and a power company. Moreover, the types of consumers are identified and distinguished. The utility functions constructed for them describe the real situation rather well, and allow the implementation of a system of incentives for load curve optimization (load shifting from a peak time of the day by bounded rational consumer). The prices in rates providing a separating equilibrium are determined.

From the artifact to the instrument to manage electricity consumption: ergo-sociological approach to the instrumental genesis of smart grids

Dans un contexte actuel marque par des enjeux de developpement durable, notamment d'efficacite energetique, le deploiement programme des reseaux electriques communicants (smart-grids) doit apporter des possibilites d’action nouvelles pour l’usager en matiere de maitrise de sa consommation d’electricite (MCE). La presente contribution s’interesse a la maniere dont la technologie smart-grid s’elabore comme une ressource pour l’activite de MCE. L’analyse de l’appropriation de ces nouvelles technologies est centrale et determinante pour comprendre quels peuvent etre les freins et les leviers au developpement de leurs usages et de l’activite de MCE. L’approche ergo-sociologique presentee propose d’etudier ce processus sous l’angle de la genese instrumentale, c’est-a-dire du passage du statut d’artefact de la technologie introduite a celui d’instrument pour l’activite de MCE. L’articulation ergo-sociologique (entre la theorie instrumentale, l'approche du processus d'appropriation issue de la sociologie des usages et la theorie de l'acteur-reseau) offre une vision elargie de cette genese a partir de trois niveaux de focale : (i) micro, base sur l’etude de l'activite mediatisee et l’elaboration de ressources pour et dans l’activite ; (ii) meso, reposant sur l’etude des pratiques sociales comme berceau de l’activite mediatisee et ; (iii) macro, s’interessant aux interactions entre actants du reseau socio-technique dans lequel l’activite mediatisee s’inscrit.