Energy Management System Data Research Articles

Estimation of Behind-the-Meter Power Loss of Renewables With EMS Data

While the energy management system (EMS) has been operated with output power measurements from all generation units (GUs) in transmission system, most renewable energy sources (RESs) in distribution system have been installed behind-the-meter (BTM). As the result, the power outputs from RESs are invisible to utilities, and they are separated from the available net load in the EMS based on the assumption that all data are accessible to utilities. Thereafter, this BTM power loss of renewables might not only prevent taking the proper control actions in time, but also cause a serious stability problem in a power system, particularly when a large amount of power from RESs in distribution systems is suddenly curtailed. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">This letter proposes several methods to estimate the amount of BTM power loss of renewables with the available EMS data. Then, the effectiveness of proposed estimation methods is verified by case study on a practical system with the real EMS data.

An Intelligent Energy Management Information System with Machine Learning Algorithms in Oil and Gas Industry

Oil and gas will continue to play an increasingly important role in global economy development and prosperity for the upcoming years. However, a fact which sometimes is ignored is that the oil and gas industry is an intensive energy-consumption industry and a major contributor to greenhouse gas emissions as well. Recently, with the arrival of the “double carbon” era, the energy-intensive production of oil and gas has caused people to pay more attention. Currently, big data technologies as well as the Internet of things (IoT) are developing at a rapid pace, and the metering infrastructure technology also shows huge advancement; oil and gas companies have built many information systems and developed many functions to acquire the energy consumption and oil production data for the aim of reducing the oil production cost via the emerging information technologies, and it turned out that these investments do improve the energy efficiency and lower oil production cost. Unfortunately, due to the different development standards, some systems store the same data with different values; so, therefore, there is a “data barrier” among these systems and somehow discount the share and analysis of these data resources. To this end, in our work, based on these rich data resources from the other information systems, we discussed an intelligent energy management information system with four machine learning algorithms to enhance the analysis of data resources over energy consumption and oil production. First, we presented the innovation of energy consumption data fusion with the method “Data Lake”; then, four different machine learning algorithms: Support Vector Machine (SVM), Linear Regression (LR), Extreme Learning Machine (ELM), and Artificial Neural Networks (ANN) are installed in the proposed system for predicting the oil and gas production and total energy consumption. In order to aggregate these data into dashboard views that help managers make decisions about operations, we also demonstrated the process of energy consumption data visualization with emerging open-source software tool ECharts. Finally, a real life application of the proposed system was summarized in case of total energy consumption prediction and the system output shows good prediction accuracy which proves the feasibility and benefits of the intelligent energy management information system.

Behavioural and environmental sustainability determinants of residential energy management information systems use

Notwithstanding appliance energy efficiency and advances in Smart Home Technologies (SHT), residential energy consumption is still on the rise. Residential Energy Management Information Systems (REMIS) that enable users to monitor, analyse, and benchmark energy use and consumption can contribute to energy savings and mitigate unnecessary consumption. Nevertheless, the withdrawal of Microsoft's Hohm and Google's Powermeter and the low adoption rate of smart meters in the US, UK, Canada, and Australia signposts missed opportunities in reducing energy intensity. Thus, the question of how to influence the successful adoption of REMIS and other related sustainable technologies captivates both researchers and practitioners. In this paper, based on a conceptual model developed by integrating the theory of planned behaviour and pro-environment behaviour research, we analysed survey data from 208 Singapore testbed eco-town apartment precinct households. The study found that environmental altruism and sustainability information exposure induce normative pressures towards REMIS use and a sense of volitional control over REMIS use. Furthermore, presence of children and the apartment size moderate the causal relationship between attitude, subjective norm and perceived behavioural control concerning REMIS adoption. These findings offer theoretical and empirical insights to cleaner production, residential energy consumption research and other sustainable technologies use that can contribute to UN's Sustainable Development Goals (SDGs). It also discusses the strategic, policy and design implications for energy and technology providers, and residential energy users.

Data Quality Test Method for Factory Energy Management System

Because data is an important factor in the software industry, how to reliably test data is important. This is even more essential for building Industry 4.0 and smart industrial complexes. This study prepares ISO/IEC 25024-based test methods and guidelines and uses them for energy management at the industrial complex level. In order to provide services by collecting energy data from industrial complexes, it is necessary to verify data quality based on data reliability and compatibility of each plant. Data quality technology needs to conform to ISO TC184/SC4/WG13 (industrial data quality standard) based technology. The study defines the data quality evaluation matrix for the energy management system of industrial parks and factories. It defines five categories and maps detailed indicators to each. The category has three detailed items, which are evaluation items for core requirements, interoperability, and conformity to standards. Each data requirement category covers functionality and reliability, usability and efficiency, and portability as data requirements in the system. Core requirements for system operation such as data consistency are basic evaluation items, and interoperability, which is the semantic compatibility of data for integrated operation of multiple sites, is verified. In addition, data quality is evaluated by verifying standard conformance. Through this evaluation system, the requirements for linking the factory energy management system data with the industrial complex energy management system can be evaluated. This can be used to monitor data quality and develop improvement technologies by developing a master data quality management technology standard suitable for industrial sites.

Development and research of rural renewable energy management and ecological management information system under the background of beautiful rural revitalization strategy

At present, rural areas in China have problems such as poor living environment, shortage of facilities, and industrial disappointment. They still use the concept and strategy of urban planning and design to manage the rural areas, which leads to the rural planning results become empty shells, which is time-consuming and laborious, but does not bring practical use to the improvement of rural life. The purpose of this paper is to better implement the rural revitalization strategy, more scientifically guide the beautiful rural construction, and more effectively restore the rural vitality. Based on the strategy of beautiful village planning under the background of rural revitalization strategy, this paper takes Shuanglongqiao Village, Fengming Town, Xichong County, Sichuan Province as the empirical research object, and comprehensively applies research methods such as literature inquiry, on-site investigation and comparative analysis. Firstly, the paper puts forward the background of rural revitalization strategy and the concept of beautiful rural planning. Based on the theory and practice of beautiful rural planning at home and abroad, it analyzes the guiding significance and enlightenment of these theories and practices on beautiful rural planning. And further reflect on the concept and strategy of planning and construction of beautiful villages; then, in combination with national policies, profoundly analyze the scientific connotation of rural revitalization strategy, on this basis, comprehensively analyze the requirements of rural revitalization strategy for beautiful rural construction, and then combine with rural planning The characteristics of the compilation system of urban planning, and the basic strategies of beautiful rural planning under the background of rural revitalization strategy. Finally, combined with the characteristics of the village and the construction needs, the paper summarizes the planning strategies suitable for the development needs and village characteristics of Shuanglongqiao Village under the background of rural revitalization strategy. On the basis of previous studies, this paper comprehensively interprets the scientific connotation of rural revitalization strategy, and conducts summative research on beautiful rural planning. It hopes to refine some beautiful rural planning concepts and strategies, and other similar Shuanglongqiao villages under the background of rural revitalization strategy. The beautiful village planning provides a new perspective and also provides some experience in building practices in other villages.

Estimation of Self-Sufficiency Rate in Detached Houses Using Home Energy Management System Data

Japan’s energy consumption in 2018 was about 2.5 times that in 1975, with the increase in the household sector being the largest at 28%. Most of primary energy is still fossil fuel, and it is urgent to reduce energy consumption in the household sector. The purpose of this paper was to identify ways to reduce household energy consumption without compromising the quality of life in residence. However, the reduction methods vary by region, building specifications, household type, equipment specifications, season, and weather. The value of this paper is based on a systematic analysis of home energy management systems (HEMS) data from about 50,000 households under various conditions. We are analyzing ways to reduce energy consumption. Few studies have analyzed this much back-up data, which is likely to lead to a reduction in CO2 emissions across the household sector. To explore ways to reduce energy consumption in this sector, the company has introduced and provided services for home energy management systems (HEMS) since 2011 and is currently collecting HEMS data for up to 50,000 households. In order to grasp the actual state of energy consumption in each household, HEMS data are systematically analyzed, necessary conditions for energy reduction and self-sufficiency rate (SSR) improvement are analyzed, and energy consumption under certain conditions is estimated using storage batteries (SB) and heat pump water heaters (HPWH). In addition, energy consumption was investigated by actual measurement and simulation for several hundred households. Since power generation and consumption vary greatly depending on the region, building specifications, household type, equipment specifications, season, weather, etc., it is necessary to analyze these factors systematically. As a conclusion, in order to improve SSR, it is necessary to (1) reduce surplus power consumption and energy consumption of heat pump water heaters (HPWHs), (2) increase solar power generation, and (3) increase the size of SB. This study contributes to the spread of advanced housing and the reduction of CO2 emissions in the household sector.

Harnessing smart meter data for a Multitiered Energy Management Performance Indicators (MEMPI) framework: A facility manager informed approach

Energy management information systems (EMIS) play a critical role in providing actionable insights into building operations, timely feedback, and—ultimately—large energy savings. Current EMIS technologies often focus on industrial applications or require large upfront investments and trained operators, therefore greatly limiting its penetration into existing buildings. This paper integrates methods from social, building, and data sciences to understand limitations of current EMIS systems and inform the design of a new Multitiered Energy Management Performance Indicators (MEMPI) framework for characterizing the energy performance of buildings. Specifically, we employed a mixed methods research approach in which we first conduct in-depth qualitative interviews of 10 facility managers and energy consultants. We utilize the insights from our interviews to inform the design of the MEMPI framework, which harnesses highly granular data from already installed advanced metering infrastructure (AMI) (i.e., smart meters). The MEMPI framework employs quantile regression to first benchmark the energy performance of buildings to each other and generate key performance indicators (KPIs). We apply the MEMPI framework to real data from 569 public school buildings in California and measure their energy performance across multiple time scales (e.g., daily, monthly, yearly). Finally, we conduct case studies to compare insights from the MEMPI framework to the perceptions of facility managers overseeing 8 schools through a mixed methods qualitative and quantitative post-interview survey. Results from the case study show that facility managers’ perceptions of the performance of their schools were largely accurate, yet the poor energy performance from certain pieces of building equipment and operating schedules was overlooked by building managers. Overall, the MEMPI framework aims to bridge the gap between data-driven energy management models and qualitative domain knowledge held by facility managers to provide more comprehensive insights into the energy performance of buildings.

A Robust Blockchain Framework for Healthcare Information System

Nearly every organization needs solution to handle sensitive data. Though there are different systems to store and transmit electronic records, users often deal with missing or tampered information when they distribute it in a public network. When the data is distributed, the traceability and governance of it throughout the globe is impractical. The evolving world of Data Security demands healthcare industry to have an infrastructure in place to deal with patient records. Regardless of the nature of present and future security risks, Blockchain Technology will play a vital role to maintain the originality of the digital data. Blockchain stores data in a decentralized, distributed ledger for achieving immutability and transparency. The system analyses a case study which aims at maintaining Healthcare records in Blockchain ledger to facilitate secure storage of the patient’s details. For efficient prediction of diabetes the Blockchain and machine learning algorithms are integrated. The various other applications of combining Blockchain technology with machine learning algorithms are Smart city applications, Forensic information storage systems, Energy data management systems, banking and finance management systems.

Research on Intelligent Management Platform for Energy Enterprises Based on Big Data Technology

This paper combines a large domestic energy company, takes energy informatics as the theoretical framework, combines the energy consumption status and structural characteristics of the enterprise, analyses the current situation of energy management in the enterprise and the problems in energy management, and uses data from the energy management information system. The exchange and information coupling mechanism connect the various information systems of the enterprise, and optimizes the design of the convection network, sensor body and sensor network to build an enterprise energy informatics model. With the big data idea as the core and the service-oriented technology architecture, the company’s real-time energy is established Data warehouse to integrate energy information of the enterprise, further reduce energy consumption and improve energy efficiency, thereby ensuring that the enterprise minimizes waste in the process of energy use, while reducing operating costs, thereby achieving the goals of energy conservation, emission reduction and environmental protection.

Assessing unregulated electricity consumption in a case study university

Energy consumption in buildings varies considerably depending on type and usage. However, monitoring and regulation of electrical power consumption due to user-related activities are not presently embedded in current English Building Regulations. These energy uses include lifts, server rooms, and small power loads, and are collectively referred to as unregulated energy usage. As part of a larger study of unregulated energy usage in university campus buildings, this paper describes the development and demonstration of a methodology for quantifying unregulated power usage using Building Energy Management System data for a case study university. The methodology was applied to four different buildings. This approach was applied to a Chemical Laboratory, a Physical Science Laboratory, a Library and an Administration Office. The results obtained revealed that electrical consumption was consistently much higher than traditional benchmarks suggest as being normal, particularly within the laboratories. Additionally, within the specific unregulated electricity case study, four rooms in the Physical Science Laboratory indicated very high unregulated electricity usage figures, averaging at 89 kWhm−2 per annum across just four rooms. Practical application: Unregulated energy research, within the Higher Education Sector, is undeveloped. Previous literature focuses on specific areas of unregulated energy, such as equipment usage. However, analysis based on different room types has not been completed on a wider scale. Hence, there is a gap in the current research. This research shows analysis of unregulated energy in different university buildings. It develops a methodology to further understanding on this undeveloped area. Additionally, it advises that unregulated energy must be calculated during a building’s design stage. By using a selection of case studies, this research furthers understanding on the overall effects of unregulated energy.

Assessment for a Distributed Monitoring System for Industrial and Commercial Applications

An energy management information system (EMIS) combines software, hardware, and data to support people in their efforts to manage energy at the process, system, facility, and enterprise level, year after year. A distributed measurement and monitoring system (DMS) is a fundamental part of an EMIS; it is constituted by the meters distributed in the switchboards of the power system. This article suggests a method of assessment of a DMS based on an indicator called level of coverage (LOC). This indicator can evaluate the LOC of a single meter or of a whole system considering the classification in significant energy uses according to the standard ISO 50001.

Renovation of Public Buildings towards nZEB: A Case Study of a Nursing Home

Recently, the European Union set new rules for the Energy Performance of Buildings emphasizing the need to facilitate the cost-effective renovation of existing buildings into nearly zero-energy buildings (nZEB). Within this framework, the aim of this paper is to investigate and evaluate the energy performance of existing public buildings before and after renovation into nearly zero-energy buildings. Since the general quality of existing buildings in Croatia has changed over different periods of construction, the presented research was conducted on a representative nursing home designed corresponding to different Croatian national building stock construction periods, based on a public sector buildings database obtained from the Croatian Energy Management Information System (EMIS). The thermal performance of the building envelope was designed to correspond to the periods of buildings built up to 1940, between 1941 and 1970, and between 1971 and 1980, taking into account characteristic building construction technology and service life. In terms of adopted energy efficiency measures, for all nearly zero-energy building renovation solutions, an energy and cost analysis was performed. The results showed that construction technology applied in different construction periods has the greatest impact on the energy performance of a building and thus on the economic and financial viability of investment in nZEB.

Cloud forecasting system for monitoring and alerting of energy use by home appliances

Inrecentyears,energy information systems have had an important role in the operational optimization of intelligent buildings to provide such benefits as high efficiency, energy savings and smart services. Interest in the intelligent management of home energy consumption using data mining and time series analysis is increasing. Therefore, this work develops an efficient web-based energy information management system for the power consumption of home appliances that monitors the energy load of a home, analyzes its energy consumption based on machine learning, and then sends information to various stakeholders. It interacts with the end-user through energy dashboards and emails. The web-based system includes a novel hybrid artificial intelligence model to improve its prediction of energy usage. An automatic warning function is also developed to identify anomalous energy consumption in a home in real time. The cloud system automatically sends a message to the user's email whenever a warning is necessary. End-users of this system can use forecast information and anomalous data to enhance the efficiency of energy usage in their buildings especially during peak times by adjusting the operating schedule of their appliances and electrical equipment.

Вдосконалення методу групування енергетичних даних у системі багаторівневого управління енергоефективністю економіки регіону

Визначено основні причини повільного виконання операцій складного групування енергетичних даних у багаторівневих розподілених системах збереження даних, основні проблеми продуктивності методу MapReduce та методи його оптимізації. Розглянуто основні вимоги до системи багаторівневого управління енергоефективністю регіону, архітектуру та компоненти такої системи, запропоновано методи її вдосконалення. Розглянуто основні характеристики енергетичних даних, методи їх опрацювання, запропоновано оптимальну структуру збереження таких даних. Наведено основні механізми та методи швидкої вставки нових даних у систему та динамічного репартиціонування для забезпечення рівномірного розподілу даних у системі. Розроблено архітектуру системи збереження та опрацювання енергетичних даних. Розроблено метод партиціонування даних на основі методу MapReduce та структуру розподіленої системи збереження даних, яка забезпечує збереження даних з однаковими значеннями полів у одній партиції даних, тобто на одному комп'ютері. Цей метод партиціонуання збирає в одну партицію записи з однаковими значеннями по полях, за якими будуть здійснюватися операції групування та агрегації. Такий підхід дає змогу зменшити час виконання та об'єми даних, які будуть транспортуватися по мережі під час виконання запитів групування та агрегації даних із використанням методу MapReduce. Також розроблена структура системи допомагає оптимізувати операції фільтрування даних.

Energy Management Enhanced ERP

During recent years, energy management has evolved to be an important corporate work area. Despite the new business opportunities for software vendors and user companies that arise from this work area, most ERP systems still only offer limited functionality for energy management. This article attempts to demonstrate these opportunities by a description of corresponding use-cases. Also, technical considerations for the integration of energy management functionalities into an ERP system are described with a focus on a system coupling approach. The intended extension is achieved by coupling a dedicated energy management information system to the ERP system. The coupling approach is exemplified through a demonstration that consists of an extended version of the abas ERP system which has been on the market for more than 30 years.

Analysis of Energy Performance and Buildings Characteristics Obtained from Croatian Energy Management Information System

Analysis of Energy Performance and Buildings Characteristics Obtained from Croatian Energy Management Information System

Semantic Meta Model for the Description of Resource and Energy Data in the Energy Data Management Cycle

Energy efficiency is a critical competitive factor. Transparency of energy consumption is the key for increasing efficiency of production. For this purpose, existing energy data management systems collect data such as power, gas or water consumption on field level, save them in databases, and aggregate them in reports. However, the identification of saving potentials and the definition of efficiency measures is carried out by energy experts and thus is dependent on a person’s knowledge. The documentation of knowledge about saving potentials and measures does not take place and relations among data and knowledge of various domains are not captured. In this paper, we provide an approach that allows the holistic capture and description of data and knowledge relations. Through the use of an ontology-based meta model, consumption data can be augmented with information about time and place of capture, data type, intended purpose and permissions, as well as interfaces to other systems and relations to knowledge elements. The semantic model is to capture relevant requirements of all information demanders within the energy data management cycle. Therefore, the model is capable of detecting efficiency deficits and retrieving relevant energy efficiency measures within a knowledge base. Thus, energy consumption data can be efficiently used and knowledge about efficiency can be sustainably preserved.

An integrated research for architecture-based energy management in sustainable airports

Abstract Energy Management (EM) has become crucial and much more complicated for airports with the introduction of various energy sources, technologies and different comfort requirements. Regarding the aviation industry as one of the major sources of global warming and air pollution, this situation becomes highly critical. However, the review of literature on Energy Management Information Systems (EMIS) for airports shows that the proposed solutions are usually domain-specific, platform-depended and away from suggesting complete solutions and architectures. Therefore, the main argument of this study is that a holistic and integrated approach should be adopted for EM in airports and we claim the notion of sustainability through the use of Enterprise Architecture (EA)-based EM. In this paper, we present the results of a two-faced research study. Action Research (AR) and Design Science Research (DSR) methods are combined to adopt an integrated approach. At the first phase, an EA is developed and evaluated, and then, this is followed by the second phase with three cases to find the potential energy savings in Istanbul Airport. Along with the findings, the primary and secondary contributions of this research brought to the EM knowledge domain are presented. Consequently, there is an important potential for energy saving in the terminal buildings, which would be approximately 70% of the total airport energy consumption. There is also a nearly 250.000 $/year potential saving, and also 121.397 $/year for the daylight time and period. This research can be seen as an initial attempt to the enhancement of sustainable airports, and therefore, it has showed the potential for using EAs as a means to improve EM in airports. We hope that this study may help researchers to obtain an overview of existing and possible approaches to sustainability through the use of EAs for EM practices.

Effective energy data management for low-carbon growth planning: An analytical framework for assessment

Readily available and reliable energy data is fundamental to effective analysis and policymaking for the energy sector. Energy statistics of high quality, systematically compiled and effectively disseminated, not only support governments to ensure national security and evaluate energy policies, but they also guide investment decisions in both the private and public sectors. Because of energy's close link to greenhouse gas emissions, energy data has a particularly important role in assessing emissions and strategies to reduce emissions. In this study, energy data management in four countries – Canada, Germany, the United Kingdom and the United States – are examined from both organizational and operational perspectives. With insights from these best practices, we present a framework for the evaluation of national energy data management systems. It can be used by national statistics compilers to assess their chosen model and to identify areas for improvement. We then use India as a test case for this framework to assess how India may adapt and evolve its energy data management systems. Its government is working to enhance India's energy data management to improve sustainable growth planning.

Energy-saving effect of automatic home energy report utilizing home energy management system data in Japan

This study assesses the effects of sending home energy reports utilizing the Home Energy Management System (HEMS) data to more than 1600 households in Japan. The treatment effect was verified using a panel data regression random effects model comparing the electricity consumption of a treatment group to which the report was sent with that of a control group that was not sent. The report was effective in winter and led to a 3.4% reduction in electricity consumption compared to the previous year in the average household. A further reduction of 5.4% for the households with higher electricity consumption for whom a significant reduction of 11.4% in the use of space heating was also observed. Although the treatment effect was not significant in summer for the average household, larger households reduced consumption by an overall average of 2%, with reductions of 6.8% and 7% in terms of space cooling and hot water use, respectively, from the previous month. In contrast, smaller households increased their space cooling consumption by more than 10% on average, which might be considered an undesirable boomerang effect. The accumulative treatment effect in a detached house group was also confirmed. Additionally, an accumulative two-year winter consumption reduction of 7.5% demonstrated the effectiveness of continual intervention.