Energy Information System Research Articles

Comparative Study of an Energy Information System and Energy Management System According to ISO 50001

Energy management, especially in communities, is attracting growing interest but is often marred by confusion between the terms “energy information system” (EIS) and “energy management system” (EMS). This ambiguity hinders understanding of their uses and roles in improving energy performance. Our objective is to clarify these terms and their contributions to energy management. We used a mixed qualitative and quantitative approach to evaluate and compare the criteria of definition, objective, functionality, and role, as well as correlated elements, such as organizational structure, responsibilities, planning, and policy, according to the requirements of ISO 50001, in addition to criteria specific to the two systems. The study revealed differences in the roles of the EIS and the EMS, with a complementarity of 40% between the two systems depending on the positioning of the EIS in relation to the elements of the EMS process. The EIS plays a crucial role in facilitating the implementation of the EMS according to ISO 50001. This clarification contributes to a better understanding of the respective roles of these systems in energy management, thus helping communities to improve their energy performance in an effective manner.

A portable application framework for energy management and information systems (EMIS) solutions using Brick semantic schema

This paper introduces a portable framework for developing, scaling and maintaining energy management and information systems (EMIS) applications using an ontology-based approach. Key contributions include an interoperable layer based on Brick schema, the formalization of application constraints pertaining metadata and data requirements, and a field demonstration. The framework allows for querying metadata models, fetching data, preprocessing, and analyzing data, thereby offering a modular and flexible workflow for application development. Its effectiveness is demonstrated through a case study involving the development and implementation of a data-driven anomaly detection tool for the photovoltaic systems installed at the Politecnico di Torino, Italy. During eight months of testing, the framework was used to tackle practical challenges including: (i) developing a machine learning-based anomaly detection pipeline, (ii) replacing data-driven models during operation, (iii) optimizing model deployment and retraining, (iv) handling critical changes in variable naming conventions and sensor availability (v) extending the pipeline from one system to additional ones.

From bricks to bytes: Verifiable data for decarbonizing the building sector

Amidst the pressing need to combat climate change and curb greenhouse gas (GHG) emissions, the building sector emerges as a pivotal sector, substantially impacting worldwide emissions. Despite efforts to improve energy efficiency and incorporate non-fossil energy sources, the sector still lags in achieving the necessary decarbonization goals. Existing Building Energy Management Systems primarily prioritize economic criteria, overlooking the vital aspect of emissions reduction. Energy Informatics and Information Systems hold the potential to bridge this gap by enabling precise and verifiable GHG emissions accounting, end-to-end real-time tracking, and automated verification within Energy Management Systems (EMS). This paper presents research on designing the advancement of EMSs in the form of a Building Energy Emission Management System (BEEMS) leveraging verifiable emission data for emission-based actions. The central research question revolves around designing BEEMS to facilitate emission-based actions based on verifiable data. Following a multi-step approach, the research methodology encompasses a comprehensive literature review and iterative evaluation of our design principles through a workshop and semi-structured interviews with experts from industry and research. The contributions include a conceptual architecture of a BEEMS and six design principles for future BEEMS development. Ultimately, this research strives to facilitate end-to-end verifiable GHG emissions management in the building sector to enable emission-based energy consumption decisions, contributing to the existing body of knowledge of the Energy Informatics field on BEEMS.

The impact of adopting an energy information system on household energy consumption: a dynamic difference-in-differences approach

ABSTRACT This study analyzes effects of adopting an energy information system on energy use based on the advanced metering infrastructure (AMI) in South Korea. Adopting the AMI was randomly assigned by the Korean Government. However, the adoption timing varies across household, making it difficult to apply an ordinary difference-in-difference (DID) approach to measure the impact, given the variation in adoption timing across households. We use a dynamic DID that is suitable for estimating a causal effect of a policy whose treatment timing differs depending upon the individual. Results indicate that AMI adoption led to a reduction in household electricity consumption, peaking within 18 months after adoption with a slight rebound effect. Our findings suggest that providing information on energy consumption and cost in the household sector can significantly reduce electricity use by as much as one quarter of average monthly consumption. Reducing electricity use in households can be achieved in several ways, including monetary incentives as well as personal and social feedback provided by adopting the AMI, implying the importance of a dissemination policy.

Exploring the energy informatics and energy citizenship domains: a systematic literature review

To effectively address the challenges posed by the increasing share of the energy sector in global greenhouse effects, the domains of energy informatics and energy citizenship play a critical role. Energy informatics aims at using information systems and channels to reduce energy consumption. However, there is a realization that the challenges posed by global greenhouse effects cannot be catered to alone by the energy information systems. Therefore, there is a need for engaging human inhabitants to actively engage toward more sustainable means (i.e., energy citizenship) thus reducing the energy sector’s share in the global greenhouse effect. This paper presents a systematic literature review (SLR) after analysis of (n = 115) articles on the topic to identify (i) the themes considered in energy informatics and energy citizenship domains, and (ii) the interconnection between energy informatics and energy citizenship domains, (iii) energy information needs among stakeholders which establish a clear interconnect with energy citizenship. These identified themes and their interconnections are critical for energy researchers, policymakers, and energy businesses to identify relevant research topics, identify energy consumers’ needs, and create just energy transition policies. The paper additionally summarizes the gaps in the state of the art by mentioning the open research questions that arise due to the identified interconnection between energy informatics and energy citizenship.

The Evolution of Energy Management Maturity in Organizations Subject to Mandatory Energy Audits: Findings from Italy

Promoting energy efficiency is a key element of the strategic commitment of the European Community. Prominent among the binding measures established by the 2012 Energy Efficiency Directive to further this vision is the requirement for large companies to conduct energy audits every four years. After receiving the second cycle of energy audit reports in December 2019, a new description of the energy situation of Italian companies was made available. This presented the previously inaccessible possibility of comparing the two situations reported in 2015 and 2019 to assess the development of energy efficiency practices in organizations subject to the legislative obligation of energy audits in the country. To this end, in collaboration with the Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), a project was initiated with the aim of developing the tools and methodologies necessary to assess in more detail the evolution that has occurred in the four years since 2015. In this paper, the findings of the analysis conducted on a significant sample of companies in Italy are presented. Through the design of a maturity model to assess the degree of progress achieved in a company’s energy management, the results of the two situations were analyzed. The analysis was deepened by assessing the progress achieved in different aspects of Energy Management: “Strategic Approach”, “Awareness, Competence, and Knowledge”, “Methodological Approach”, “Organizational Structure”, “Energy Performance Management and Information System”, and “Best Practices”. Furthermore, the observed variations were statistically tested using a pairwise t-test to make statistical inferences about the maturity of the total population of Italian enterprises under legislative obligation. The results have shown an increase in overall energy management maturity in each maturity dimension.

A Photovoltaic System Model Integrating FAIR Digital Objects and Ontologies

Smart grids of the future will create and provide huge data volumes, which are subject to FAIR (Findable, Accessible, Interoperable, and Reusable) data management solutions when used within the scientific domain and for operation. FAIR Digital Objects (FDOs) provide access to (meta)data, and ontologies explicitly describe metadata as well as application data objects and domains. The present paper proposes a novel approach to integrate FAIR digital objects and ontologies as metadata models in order to support data access for energy researchers, energy research applications, operational applications and energy information systems. As the first example domain to be modeled using an ontology and to get integrated with FAIR digital objects, a photovoltaic (PV) system model is selected. For the given purpose, a discussion of existing energy ontologies shows the necessity to develop a new PV ontology. By integration of FDOs, this new PV ontology is introduced in the present paper. Furthermore, the concept of FDOs is integrated with the PV ontology in such a way that it allows for generalization. By this, the present paper contributes to a sustainable data management for smart grid operation, especially for interoperability, by using ontologies and, hence, unambiguous semantics. An information system application that visualizes the PV system, its describing data and collected sensor data, is proposed. As a proof of concept the details of the use case implementation are presented.

Comparative study between the Energy Information System (EIS) and the Energy Management System (EnMS) according to ISO 50001

Comparative study between the Energy Information System (EIS) and the Energy Management System (EnMS) according to ISO 50001

GIS-Based Wind and Solar Power Assessment in Central Mexico

In Mexico, the economic and industrial development is in the center and north; this represents more than 50% of the country’s total consumption. Data on population and energy consumption will be obtained from the following sources: the National Institute of Geography and Statistics (INEGI), and the Energy Information System. Regarding meteorological data, two databases are used: the Automatic Weather Stations (AWS) (for solar irradiance data) and the MERRA-2 reanalysis data (for wind data). These data will be analyzed for use in a geographic information system (GIS) using kriging interpolation to create maps of solar and wind energy. The area studied includes the following states: Mexico City, Puebla, State of Mexico, Hidalgo, Morelos, Zacatecas, Queretaro, San Luis Potosi, Guanajuato, Aguascalientes and Tlaxcala. The results showed that the areas with the highest solar potential are Hidalgo, Estado de México, Morelos, northern Puebla, southern Queretaro, northwestern Guanajuato, and northern Zacatecas, with 5.89 kWh/m2/day, and the months with the highest solar potential are March, April, May, and June. Regarding wind potential, the maximum wind power density is in Puebla, with 517 W/m2, and the windy season in central Mexico spans June, July, August, September, October, and November.

Building Analytics Tool Deployment at Scale: Benefits, Costs, and Deployment Practices

Buildings are becoming more data-rich. Building analytics tools, including energy information systems (EIS) and fault detection and diagnostic (FDD) tools, have emerged to enable building operators to translate large amounts of time-series data into actionable findings to achieve energy and non-energy benefits. To expedite data analytics adoption and facilitate technology innovation, building owners, technology developers, and researchers need reliable cost–benefit data and evidence-based guidance on deployment practices. This paper fulfills these needs with the energy use and survey data from a wide-ranging research and industry partnership program that covers thousands of buildings installed with analytics tools. The paper indicates that after two years of implementation, organizations using FDD tools and EIS tools achieved 9% and 3% median annual energy savings, respectively. The median base cost and annual recurring cost for FDD are USD 0.65 per square meter (m2) (USD 0.06 per square foot [ft2]) and USD 0.22 per m2 (USD 0.02 per ft2), and are USD 0.11 per m2 (USD 0.01 per ft2) and USD 0.11 per m2 (USD 0.01 per ft2) for EIS. The common metrics and analyses that are used in the tools to support the discovery of energy efficiency measures are summarized in detail. Two best practice examples identified to maximize the benefits of tool implementation are also presented. Opportunities to advance the state of technology include simplified data integration and management, and more efficient processes for acting on analytics outputs. Compared with previous efforts in the literature, the findings presented in this paper demonstrate the effectiveness of building analytics tools with the largest known dataset.

Exploring the Integration Model of Industry Chain Information System Based on Energy Internet and Its Key Technologies.

Many enterprises have elevated industrial chain management to an important strategic factor for successful operation in a competitive market environment. There are many reasons for the low success rate of enterprise information construction, one of which is that in the process of information construction, only the research of information technology and the construction of information systems are emphasized, but not the integrated management of information resources, and the strategy, mode and method of information resource management are not studied. As a solution for sustainable energy development in the future, the energy Internet has become a hot topic of research in academia and industry today. Integrated innovation, at the enterprise level, is the key to improving core competitiveness and maintaining a sustainable competitive advantage. In order to achieve a deep integration of information technology and energy technology, the energy Internet must be supported by a corresponding system, i.e., an energy information system. In this paper, we propose an information system integration model based on the nature of the industry chain, describe its functional components, and analyze the key technologies of integration. The results show that the integrated model achieves the highest value of each output solution in the time period of 4h, and the calculated transmission capacity of the line is 0.7pu The integrated model of the information system is optimal for the adaptation of the energy environment of each distributed power source in the energy Internet. Therefore, this study provides theoretical support and practical reference for further research on the construction and mechanism of the integrated energy Internet in China's theoretical and business communities, and provides important insights into the construction of the mechanism and countermeasures for the integration of the industrial chain in energy Internet enterprises.

A functional triboelectric nanogenerator based on the LiCl/PVA hydrogel for cheerleading training

ABSTRACT Triboelectric nanogenerators (TENGs) are a novel energy harvesting, sensing system, and information systems device. This work describes the construction of the lithium chloride (LiCl)/polyvinyl alcohol (PVA) hydrogel triboelectric nanogenerator (LP-TENG) with easy manufacturing, excellent electrical output, and a wide range of sensing applications. The addition of LiCl powder enhances the PVA hydrogel crosslinking and increases the composite hydrogel's elastic properties. Additionally, the LiCl powder generates microchannels on the hydrogel's surfaces, which enhances ion transport and adds triboelectric outputs through a streaming vibration possible mechanism. Following the single-electrode mode, the LP-TENG's open-circuit voltage reaches up to 276 V. Due to good tensile properties, the LP-TENG shows a monotonically increasing relationship between extensible length and output voltage. Therefore, the remarkable stretchability and ultrahigh sensitivity to mechanical stimulation of the LP-TENG are used to illustrate applications in cheerleading monitoring and human mechanical energy obtaining.

Development and Demonstrational Instantiation of a Method for the Structured Content Analysis of Smartphone Apps

The publication discusses the adaptation of dedicated known methods for structured content analysis for the usage of smartphone apps, taking into account the specifics of these dynamic media types and resulting consequences for the procedure and content rating. The background is the increasing popularity of apps and the growing demand for precise analysis of the status quo regarding the content and functions of apps on the market, esp. within design-oriented business informatics. Within the literature, no uniform, comparable and systematic approach to this could be identified so far. Therefore, an existing standard for the evaluation of apps from the medical context has been adapted for general application to apps. This provides a helpful procedure for researchers, practitioners, and students and ensures consistency across studies. The feasibility of the proposed method and assessment standard were confirmed by a successful demonstrational instantiation based on an app review of energy information systems for private customers.

Missing data imputation using mixture factor analysis for building electric load data

We propose a mixture factor analysis (MFA) method for estimating missing values in building electric load data. Buildings consume a tremendous amount of energy. Thanks to the recent advances in data technologies such as machine learning and applied statistics, data-driven approaches to making buildings more energy-efficient become a major research area. However, building electric load data suffer from quality issues due to data missing originated from malfunctioning sensors, network stability, and other environmental causes. We note that data missing can occur even under advanced Internet Technology (IT) systems such as energy information systems (EIS) and energy management systems (EMS) due to signal stability and low-speed computers. The existence of missing data may significantly affect building operations, causing inaccuracy in evaluating building status and forecasting future electric demands. In this respect, dealing with missing data problems should be as important as developing highly accurate forecasting algorithms. While investigating load data, we find that building electric loads exhibit distinct patterns with cyclic rotations that we can take advantage of in both model design and selection stages. Motivated by the finding, unlike the previous studies designed for general time-series data, we propose a novel data imputation model to represent patterns and their cyclic rotations in electric load data. Simulation studies reveal that the proposed model works well when the time window size is a divisor of the cycle length, which significantly reduces model selection efforts. Numerical results with two real data sets justify our findings and the performance of the proposed approach against benchmark methods.

Research on Computer Assisted Collaborative Scheduling of Self-Optimization Collection by Artificial Intelligence

Based on the energy Internet, the paper studies the interest needs of energy Internet providers and the service needs of different users from the perspective of data collection "convenience, interaction, and automation", and realizes the interconnection of customer energy information and new collection systems. Based on the acquisition master station, communication channel, energy Internet equipment papers to achieve the existing acquisition system architecture design. Provide technical reference for the reasonable planning and collaborative scheduling of power big data wide-area concurrent computing task flow, and comprehensively improve the processing efficiency of power big data wide-area concurrent computing task flow.

Assessing Hydraulic Fracking Issues in the US South East and Western Region

In the last several years, the choice of hydrological fracking as an alternative method of nonrenewable energy production in the US oil sector continues to gain currency across regions especially the Southeast and the West. In a country where fracking is no longer deemed as an exercise on the fringe amidst unprecedented expansion, economic boom, and ecological liabilities. The use of fracking techniques in shale fields remains so widespread across different states from California to Mississippi that it now constitutes 60% of the nation’s oil and gas output in the past two decades. This occurred in the face of favorable regulatory environments that catapulted the US atop global ranking of oil producers. While this has resulted in ample generation of revenues and job prospects in the respective states, communities in those places have endured grim impacts and risks on their ecosystems in the form of pollution, degradation, hydrological stress, induced seismicity, land disturbance and greenhouse gas emissions. Aside from efforts of the sector, regulatory agencies, and other stakeholders in the search for a common ground on the issues. The mounting ecological liabilities has in many cases aggravated tensions between affected communities and the oil sector. Yet, very little studies exist on the vulnerability of the study area to the impacts of hydraulic fracking using mix scale method of Geographic Information Systems (GIS) and energy statistics. Even when data is available, the sketchy nature tends to mar analytical proficiency given the lack of an accessible regional energy information system. Accordingly, this enquiry will fill that void by assessing the issues in hydraulic fracking in the study area. Emphasis are on the issues, trends, factors, impacts and efforts using techniques of GIS and descriptive statistics. Just as the results revealed a surge in production activities and revenues, the impacts consist of sizable use of water and chemicals together with extensive pollution, the disturbance of fragile landscapes and ecosystem decline. Additionally, GIS mappings pinpointed a gradual spread of production activities and concentration of risks across states in the zone due to several socio-economic and physical elements located withing the larger energy structure. To remedy the situation, the paper proffered solutions ranging from ecological monitoring to the design of a regional energy information system, effective policy, community participation/education of the public and the formation of an interagency task force.

A Data Analytics-Based Energy Information System (EIS) Tool to Perform Meter-Level Anomaly Detection and Diagnosis in Buildings

Recently, the spread of smart metering infrastructures has enabled the easier collection of building-related data. It has been proven that a proper analysis of such data can bring significant benefits for the characterization of building performance and spotting valuable saving opportunities. More and more researchers worldwide are focused on the development of more robust frameworks of analysis capable of extracting from meter-level data useful information to enhance the process of energy management in buildings, for instance, by detecting inefficiencies or anomalous energy behavior during operation. This paper proposes an innovative anomaly detection and diagnosis (ADD) methodology to automatically detect at whole-building meter level anomalous energy consumption and then perform a diagnosis on the sub-loads responsible for anomalous patterns. The process consists of multiple steps combining data analytics techniques. A set of evolutionary classification trees is developed to discover frequent and infrequent aggregated energy patterns, properly transformed through an adaptive symbolic aggregate approximation (aSAX) process. Then a post-mining analysis based on association rule mining (ARM) is performed to discover the main sub-loads which mostly affect the anomaly detected at the whole-building level. The methodology is developed and tested on monitored data of a medium voltage/low voltage (MV/LV) transformation cabin of a university campus.

Civilization as a Large-Scale Energy-Information System

The world is a triadic meta-system: nature - society - man, in which all components are linked by material, energy, and information relationships. These system subjects and their relationships are civilization structures represented by a "community" in our planetary House, i.e., Ecos (from the Greek ‘oikos’ – dwelling in various zones of the earthly oecumene). Civilization is a set of socio-natural factors, the system of life, and cultural-and-mental relations in the eco-system in its certain evolution stage. Regional and mental features (like apartments in an apartment house) characterize certain historical and geographical types of civilization, among which the Atlantic, East-Eurasian, and Islamic ones stand out. All of them are in different stages of their evolution and differ in their material level, the organization of social life, and the target vision of their arrangement. The current stage of the civilization development is characterized by a thorough transformation of all its types under the influence of regular cyclicality and change in the dominant factors of socio-natural relations in the planetary House, given the qualitatively new features of modernity - massive digitalization as a way of integrating the real (material and power energy) and verbal (subtle-energy and information) worlds. The paper briefly examines historical aspects of the cyclical development of civilization (using the example of its East-Eurasian type) and presents the author's target vision (foresight) of its future for the time horizon until the middle of the 21st century.

Development and Implementation of Fault-Correction Algorithms in Fault Detection and Diagnostics Tools

A fault detection and diagnostics (FDD) tool is a type of energy management and information system that continuously identifies the presence of faults and efficiency improvement opportunities through a one-way interface to the building automation system and the application of automated analytics. Building operators on the leading edge of technology adoption use FDD tools to enable median whole-building portfolio savings of 8%. Although FDD tools can inform operators of operational faults, currently an action is always required to correct the faults to generate energy savings. A subset of faults, however, such as biased sensors, can be addressed automatically, eliminating the need for staff intervention. Automating this fault “correction” can significantly increase the savings generated by FDD tools and reduce the reliance on human intervention. Doing so is expected to advance the usability and technical and economic performance of FDD technologies. This paper presents the development of nine innovative fault auto-correction algorithms for Heating, Ventilation, and Air Conditioning pi(HVAC) systems. When the auto-correction routine is triggered, it overwrites control setpoints or other variables to implement the intended changes. It also discusses the implementation of the auto-correction algorithms in commercial FDD software products, the integration of these strategies with building automation systems and their preliminary testing.

Evaluating Geothermal Energy Potentials in the US Western Region with GIS

In an era saddled by mounting energy dependency and insecurity, some are not only turning to renewable geothermal energy to ensure access, but it is growing in usage in the Western region of the United States of America. Notwithstanding the United States role as a major producer of geothermal resources globally, geothermal energy has over the last several years found ample use in various spheres of daily endeavors within communities. The surging demand is much so that, it now contributes quite significantly in the generation of electricity to power households and farming activities and to sustain domestic supplies essential to segments of the economy for communities from California to Nevada. With much of the production levels, installations and plant capacities of geothermal power fully entrenched in the western region of the country considering the geologic and physical attributes, California stands out as the largest producer of geothermal power compared to its neighbors in the western region. For that, the demand for geothermal resource has been recurrent in quite a few counties over the years on the California side of the US Western region. In the face of widespread demands in the region, geothermal energy infrastructure in the form of new plants has gradually penetrated different states under varying level of production and installed capacities to boost energy security and sustain development of the areas. Even at that, very little has been done in the literature to undertake a regional assessment of the potentials of geothermal energy using a mix scale approach of spatial analysis anchored in Geographic Information Systems (GIS). Accordingly, this study will fill that void in research by utilizing mix scale tools of GIS and descriptive states in analyzing the geothermal energy potentials of states in the Western region of the United States. Emphasizing the issues, trends, impacts, production, factors and future efforts through recommendations, the results point to growing usage and changes in a whole set of energy indicators ranging from production and demands, installed capacity to others over time in the region with much of that in California coupled with an uptick in geothermal portfolio. Additionally, the GIS mapping of the trends highlights gradual dispersion of changing patterns in consumption and presence of infrastructural facilities visibly concentrated in a cluster of areas located in the western region. With all these attributed to a host of socio-economic and physical factors, the paper proposed suggestions ranging from the design of a regional geothermal energy information system, the education of the public and more infrastructural development in the sector.