Energy-related Information Research Articles

All-in-one flexible paper-based self-powered energy and display system employing complementary properties of printed PEDOT:PSS/PANI:PSS dual-electrode with a dual-sided triboelectric nanogenerator

A Self-powered energy and display system (SPEDS) has been developed by integrating functionalities of energy harvesting, storage, and multicolor display, heralding innovative solutions for integrated electronic devices. An all-in-one multi-information SPEDS is proposed through incorporating a multicolor electrochromic supercapacitor device (ESCD) with a dual-sided triboelectric nanogenerator (TENG), all screen-printed on a paper substrate. The synergistic enhancement in the conductive, capacitive, and electrochromic performance is realized by exploiting the complementary properties of PEDOT:PSS/PANI:PSS to form a simplified structure of ESCD, in which the electrode and electrochromic layers are integrated into one shared layer, resulting in a wide color gamut and an enhanced capacitive performance of 3.4 mF/cm². Electrochromic electrodes having identical composition exhibit independent color changes at the anode and cathode, thereby featuring a combined color display indicating multi energy-related information. Moreover, the conductive layer fabricated through patterned printing on the paper substrate, serves as both the electrode layer for TENG and the interconnect circuitry with the prepared ESCD. Furthermore, a double-sided TENG was realized by exploiting the triboelectric properties of the paper substrate and then a self-powered energy display wristband was constructed, exemplifying a novelty application utilizing the multi-information indicator properties for energy display derived from the dual-sided friction power generation.

Gated Recurrent Unit (GRU) for Forecasting Hourly Energy Fluctuations

In the current digital era, energy use undeniably supports economic growth, increases social welfare, and encourages technological progress. Energy-related information is often presented in complex time series data, such as energy consumption data per hour or in seasonal patterns. Deep learning models are used to analyze the data. The right choice of normalization method has great potential to improve the performance of deep learning models significantly. Deep learning models generally use several normalization methods, including min-max and z-score. In this research, the deep learning model chosen is Gated Recurrent Unit (GRU) because the computational load on GRU is lighter, so it doesn't require too much memory. In addition, the GRU data is easier to train, so that it can save training time. This research phase adopts the CRISP-DM methodology in data mining as a solution commonly used in business and research. This methodology involves six stages: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. In this research, the model was obtained using five attribute selection, which applied 2 normalization methods: min-max and z-score. With this normalization, the GRU model produces the best MAPE of 3.9331%, RMSE of 0.9022, and R2 of 0.9022. However, when using z-score normalization, the model performance decreases with MAPE of 10.4332%, RMSE of 0.7602, and R2 of 0.4213. Overall, min-max normalization provides better performance in multivariate time series data analysis.

Survey of Cybersecurity in Smart Grids Protocols and Datasets

Smart grids are two-way communications grids that converge Information Technology (IT) and Operational Technology (OT) to transfer energy-related information between different industry components within the grid. Smart grids have changed the energy sector by increasing sustainability, efficiency and integrating renewable energy sources. However, smart grids are vulnerable to IT-related attacks because they rely on Information and Communication Technology (ICT). By surveying relevant papers and evaluating accessible statistics, this study explores cybersecurity in smart grids by examining current communication protocols and standards. We carefully compile various datasets with general information about four of the most smart grid-related datasets. Our study and conclusions address the key components of a smart grid and offer information that can help create cybersecurity plans specifically for smart grids. This research contributes to the discourse on smart grid security, which is important for preserving the stability of contemporary energy systems.

A Learning Ecology Perspective of Energy Literacy among Youth: A Case Study from Alabama High Schools

Developing energy literacy and pathways for youth to clean energy careers are vital for sustainable development, aligning with various Sustainable Development Goals (SDGs), from climate action to quality education. Despite the increasing focus on nurturing energy-literate youth, there is a lack of comprehensive insights into how students develop their energy literacy within diverse learning environments. This study addresses the research question of what factors across various learning environments play a significant role in the development of energy literacy among youth. To address the research question, we develop a conceptual framework for understanding the development of energy literacy among high school students based on a learning ecology perspective. Focusing on students’ energy literacy, encompassing information search, attitudes, behaviors, and knowledge of energy, we examined the influence of school-based clean energy program participation, virtual information sources, and interactions with peers and family. Furthermore, we conducted an empirical analysis to identify factors associated with energy literacy among high school students from Alabama high schools. The student sample was intentionally recruited from case study high schools in Alabama that implemented a clean energy education pilot project. This sample includes both program participants and non-participants who completed pre- and post-program surveys, resulting in a total of 189 survey responses. Findings indicate a positive association between the school-based clean energy program and a significantly higher increase in energy-related information searches among program participants compared with non-participants. The virtual learning setting, especially video platforms, was also significantly associated with students’ energy-related information searches and positive attitudes toward energy. Findings also indicate that family relationships and pre-existing STEM interests significantly determine youth energy literacy. These findings provide valuable insights for the development and expansion of future energy education programs. If the energy education program incorporates these crucial learning factors and establishes an interconnected learning environment, the convergence of multiple learning aspects within the program can foster a synergistic learning ecosystem for youth energy literacy.

Examining Preference for Energy-Related Information through a Choice Experiment

Many studies have shown that providing information on energy consumption to a household is effective, to some extent, in encouraging its energy conservation behavior. These studies provided information free of charge. However, depending on the type of information, a household must bear costs, such as installing the necessary equipment to obtain the information. Are people willing to pay for the information? In this study, a questionnaire survey was conducted to examine willingness to pay (WTP) for energy-related information using a choice experiment. The data were analyzed using conditional logit and latent class models. Positive WTP was estimated for information on the total energy consumption amount for the entire house, detailed electricity usage amount for each major home appliance, electricity rates by time zone, and power source composition of electricity. No significant positive results were obtained for comparison with the other households, as the class that accounted for about 40% of the analyzed sample had negative WTP for this information. When electricity companies provide comparative information, it is better to carefully consider how and to whom they provide it. The results of the latent class model show that preferences vary among classes. Although some preference variations exist, some households have a positive WTP for information on energy consumption.

Understanding the role of digitalization and social media on energy citizenship.

The digitalisation of the energy domain can bring forth numerous aspects of the energy transition that can boost the emergence of energy citizenship, information sharing, and improved decision-making processes. However, this is premised on citizens being able to make sense of (digital) information. Hence, this paper proposes a link between energy informatics and energy citizenship via energy literacy, considering the cognitive and affective aspects of energy literacy and their relation to behaviour and action. By doing so, this paper aims to understand how the use of energy-related information and social media within five different case studies from the GRETA project can impel energy citizenship. This paper approaches this rationale through different means: (a) structured interviews to understand how citizens understand and make use of energy information within the case studies; (b) topic modelling on the content of those interviews to identify common factors that might spur on hinder behaviour change towards energy citizenship; and (c) social media content analysis to identify key energy-related topics of discussions among citizens around the globe and assess the role of social media as a tool for energy citizenship. As a result, this paper identified some key takeaways to improve the delivery of energy-related information to energy citizens for enhanced energy citizenship. These takeaways allow to conclude that it is fundamental to surpass the formal boundaries of techno-economic constructs and start addressing qualitative/subjective constructs (e.g., emotions, affections, and feelings) to foster energy citizenship. Also, these takeaways could be translated into social mechanism principles in the design of frontend energy-related digital platforms for improved end-user interactions and energy citizenship. Finally, this paper recognised the need to incentivise energy citizens to use social media for consuming energy-related information, and the need to formulate coordinated and coherent response strategies for disseminating energy-related information.

DEVELOPMENT AND TESTING OF THE CITYJSON ENERGY EXTENSION FOR SPACE HEATING DEMAND CALCULATION

Abstract. 3D city models are frequently used to acquire and store energy-related information of buildings for energy applications. In this context, CityGML is the most common data model, and the Energy ADE, one of its most complex extensions, provides a systematic way of storing detailed energy-related data in XML format. Contrarily, even though CityGML’s JSON-based encoding, CityJSON, has an extension mechanism, an energy-related CityJSON Extension is missing. This paper, therefore, presents the first results of the development of a CityJSON Energy Extension and space heating demand calculation is utilized as the use case. The simplified version of the Energy ADE, called the Energy ADE KIT profile, is used to create a semi-direct translation to the CityJSON Energy Extension. This Extension is then validated through the official validator of CityJSON and the use case, and improvements are made considering the validation results. The space heating demand is calculated according to the Dutch standard NTA 8800 for a subset of Rijssen-Holten in the Netherlands although the solar gains calculation requires further review. The results show that the final CityJSON Energy Extension provides full support for space heating demand calculations based on the NTA 8800 and eliminates the deep hierarchical structure of the Energy ADE. A comparison on CityJSON file sizes shows a 25.2 MB increase after the required input data is stored in a CityJSON + Energy Extension file, which is not significant considering the high amount of data stored in the file. Overall, this paper shows that the CityJSON Energy Extension could provide an easy-to-use alternative to the CityGML Energy ADE.

Exploring channels and gaps in information dissemination and acquisition among energy scientists and the public: The southeast Asian context.

This study investigates the flow of energy-related information, which plays a vital role in promoting the public understanding and support for various energy sources. Through 12 focus group discussions with the public and energy experts, this study found that energy information flows from scientists to the public through both direct (e.g., roadshows, scientists’ blogs) and indirect (via agents, e.g., school, news media) channels. However, communication gaps remain between scientists and the public. First, the public commonly obtains information from personal experience and the media but not directly from scientists. Second, while the public stressed the importance of mass media and social media, only a few experts reported writing news commentaries or making social media posts about energy. Third, while scientists emphasize their relationships with the government and other agencies in disseminating information, the public shows relatively weak trust in these agencies. Implications are made for future research and public communication on energy issues.

Energy data generation with Wasserstein Deep Convolutional Generative Adversarial Networks

Residential energy consumption data and related sociodemographic information are critical for energy demand management, including providing personalized services, ensuring energy supply, and designing demand response programs. However, it is often difficult to collect sufficient data to build machine learning models, primarily due to cost, technical barriers, and privacy. Synthetic data generation becomes a feasible solution to address data availability issues, while most existing work generates data without considering the balance between usability and privacy. In this paper, we first propose a data generation model based on the Wasserstein Deep Convolutional Generative Adversarial Network (WDCGAN), which is capable of synthesizing fine-grained energy consumption time series and corresponding sociodemographic information. The WDCGAN model can generate realistic data by balancing data usability and privacy level by setting a hyperparameter during training. Next, we take the classification of sociodemographic information as an application example and train four classical classification models with the generated datasets, including CNN, LSTM, SVM, and LightGBM. We evaluate the proposed data generator using Irish data, and the results show that the proposed WDCGAN model can generate realistic load profiles with satisfactory similarity in terms of data distribution, patterns, and performance. The classification results validate the usability of the generated data for real-world machine learning applications with privacy guarantee, e.g., most of the differences in classification accuracy and F1 scores are less than 8% between using real and synthesized data.

Discovering, processing and consolidating housing stock and smart thermostat data in support of energy end-use mapping and housing retrofit program planning

• Systematic workflow to extract energy-related information from different buildings related datasets is developed. • Different datasets are considered, processed, and information related to building energy simulation are extracted. • Suggestions to improve the building and heating permit data collection at municipality level are made. • Recommendations for effective housing retrofit and energy planning are made. There is growing interest in energy mapping amongst municipal planners and policymakers to accelerate greenhouse gas (GHG) emissions reduction program implementation. Responding to this interest partly involves addressing challenges related to building stock data collection and processing. The present study, carried out in conjunction with Natural Resources Canada's Canadian Energy End-use Mapping (CEE Map) project, aims to develop the inputs for housing energy modeling and mapping using property assessment data, building, heating permit data, and smart thermostat data. In this context, a systematic workflow is presented to extract useful information from various data sources to support housing energy simulations and municipal retrofit program planning. Permit data analysis supported refinement of housing data in Kelowna's urban digital twin. Results from building permit data analysis serve to update housing attributes including construction year and dwelling type. Results from heating permit analysis suggest that 17.5% of Kelowna dwellings could be potential candidates for heating system upgrades. Regarding thermostat setpoint temperature and occupancy for energy simulations, results obtained from smart thermostat data analysis were compared with EnerGuide Rating System (ERS) assumptions to investigate the potential improvements that can be made in energy simulation inputs. Comparative results indicated variations of up to 2 °C between smart thermostat data and EnerGuide assumptions for thermostat setpoint temperatures. Also, smart thermostat data suggests that 87% of dwellings were occupied for more than 50% of the time, whereas in ERS, occupancy is assumed to be 50%. Together, the overall data workflow and the detailed investigation of different datasets contributes to the development of a best practice methodology for housing energy modeling and mapping for municipalities in support of GHG emission reductions. Further, recommendations for permit data collection and scope for future research works are provided.

Energy management based on multi-agent deep reinforcement learning for a multi-energy industrial park

Owing to large industrial energy consumption, industrial production has brought a huge burden to the grid in terms of renewable energy access and power supply. Due to the coupling of multiple energy sources and the uncertainty of renewable energy and demand, centralized methods require large calculation and coordination overhead. Thus, this paper proposes a multi-energy management framework achieved by decentralized execution and centralized training for an industrial park. The energy management problem is formulated as a partially-observable Markov decision process, which is intractable by dynamic programming due to the lack of the prior knowledge of the underlying stochastic process. The objective is to minimize long-term energy costs while ensuring the demand of users. To solve this issue and improve the calculation speed, a novel multi-agent deep reinforcement learning algorithm is proposed, which contains the following key points: counterfactual baseline for facilitating contributing agents to learn better policies, soft actor–critic for improving robustness and exploring optimal solutions. A novel reward is designed by Lagrange multiplier method to ensure the capacity constraints of energy storage. In addition, considering that the increase in the number of agents leads to performance degradation due to large observation spaces, an attention mechanism is introduced to enhance the stability of policy and enable agents to focus on important energy-related information, which improves the exploration efficiency of soft actor–critic. Numerical results based on actual data verify the performance of the proposed algorithm with high scalability, indicating that the industrial park can minimize energy costs under different demands.

C60ThSe2/ITO interface formation: photoemission-based charge-transfer recognition for organic electronics applications.

Within the presented work, we propose a complex photoemission-based approach for the investigation of the C60ThSe2 dyad (C60ThSe2)/indium-tin oxide (ITO) interface formation. For surface topography and basic morphology determination, atomic force microscopy was utilized, and the results showed that C60ThSe2 agglomerated into close-to-spherical crystallites and the island-like growth was the dominant type for fullerene growth on the ITO substrate. Further, detailed X-ray and UV-photoelectron spectroscopies (XPS, UPS) were used for thorough characterization of the chemical and electronic properties of the investigated structures. Experiments were conducted by means of cyclic voltammetry and UV-VIS techniques for both deposition purposes and for determination of the basic electronic structure. As a result, we present the detailed characterization of the chemical and energy structures with a clear designation of the mutual influence of both materials on their counterparts. Among others, the accurate photoemission signal decomposition of the overlapping signals was done with respect to obtaining the energy-related information depth. The obtained data clearly showed that an interface dipole (0.56 eV) was created between the ITO substrate and organic overlayer at the ultrathin coverage stage. Since our results point out the most probable charge-carrier relocation in the vicinity of the interface, this together with the dipole existence should be taken into account while creating energy-level cascades for various (e.g., photovoltaic or organic electronic) applications. The work may also provide insights for engineers working with a vast range of organic-based electronics while designing devices based on fullerene/ITO hybrid structures.

Pursuing a net-zero carbon future for all: Challenges for commercial real estate

Pursuing a net-zero carbon future for all: Challenges for commercial real estate

Design, implementation, and testing of an energy consumption management system applied in Internet protocol data networks

To model the power measurement conditions of an energy system, an initial reference should be made within the specification of a generic energy consumption (EC) model. This EC model is based on the energy state of a system comprising goods, networks, and services, which supports energy management capabilities. Therefore, a prototype tool to record power consumption (PC) at the device level is designed and implemented, thereby enabling devise use optimization. The core is to collect energy-related information in internet protocol networks that support the simple network management protocol, in addition to the standard proposed in RFC-7460, and the green information technology. The information obtained is related to the energy consumed by devices that support those technologies. Then, the information is processed to evaluate different PC characteristics of the network. This research impacts the knowledge gap of information system management, where the objective sought is to establish metrics and methodological schemes for obtaining data regarding measurable behaviors in the EC field

Task Scheduling of Material-Handling Manipulator for Enhancing Energy Efficiency in Flow-Type FMS

Energy savings and reduction in environmental burdens are necessitated to enhance sustainable manufacturing performances. Not only should energy consumption in the factory be visualized, but also a mechanism, by which in-process production and energy-related information measured in the shop floor are fed back into planning/scheduling decision-making, must be established to improve the energy efficiency during manufacturing execution. This study addresses the effect of scheduling on the improvement of energy efficiency in manufacturing by connecting a developed measurement and control platform with a real manufacturing system. The manufacturing system testbed utilized in this study forms a simple flow-type flexible manufacturing system composed of automated manufacturing cell with a CNC lathe, material-handling manipulator, and vertical machining center. We focus on the task scheduling of the material-handling manipulator, which yields a job sequence, and the effect of task scheduling of the manipulator on the energy efficiency and productivity of the entire manufacturing system.

An “ideological fantasy”: how market discourses confuse, obscure and deflect consumers’ attention away from the science of energy conservation

ABSTRACT Our research focuses on sustainable energy use and our aim is to establish if communication at critical social interfaces is providing information that motivates pro-environmental behaviour change. Here we focus on one key site of energy-related information exchange – the interface between energy retailer and domestic energy consumer. Our interpretive analysis involved a comparative discourse analysis of consumers’ accounts of their communication with their energy retailers and exposed a dialogic failure, perpetrated by an ‘ideological fantasy’ of consumer empowerment that appears to prevent scientific information from being translated into sustainable energy practices. We discuss what we have learnt and close by briefly considering how our learnings might help avoid perpetrating similar fantasies of empowerment when scientific texts are exchanged at the academy-societal interface. In doing so, we link our work to this ICA panel’s theme which focuses on open science.

Experiments on Model-Based Software Energy Consumption Analysis Involving Sorting Algorithms

Although energy has become an important aspect in software development, little support exists for creating energy-efficient programs. One reason for that is the lack of abstractions and tools to enable the analysis of relevant properties involving energy consumption. This paper presents the results of some experiments involving the gathering, modelling, and analysis of energy-related information, in particular, the costs of executing certain parts of a software. We combine some existing free and open-source tools to carry out the experiments, extending one of them to handle energy information. Our experiments consider a comparison of energy consumption of Java implementations of the Bubble Sort, Insertion Sort and Selection Sort algorithms using different data structures. We show how to combine an energy measurement tool and a model analysis tool to carry such a comparison. Based on this support and on our experiments, we believe this is a first step to allow developers to start creating more energy-efficient software.

Tanycytes and the Control of Thyrotropin-Releasing Hormone Flux Into Portal Capillaries.

Central and peripheral mechanisms that modulate energy intake, partition and expenditure determine energy homeostasis. Thyroid hormones (TH) regulate energy expenditure through the control of basal metabolic rate and thermogenesis; they also modulate food intake. TH concentrations are regulated by the hypothalamus-pituitary-thyroid (HPT) axis, and by transport and metabolism in blood and target tissues. In mammals, hypophysiotropic thyrotropin-releasing hormone (TRH) neurons of the paraventricular nucleus of the hypothalamus integrate energy-related information. They project to the external zone of the median eminence (ME), a brain circumventricular organ rich in neuron terminal varicosities and buttons, tanycytes, other glial cells and capillaries. These capillary vessels form a portal system that links the base of the hypothalamus with the anterior pituitary. Tanycytes of the medio-basal hypothalamus express a repertoire of proteins involved in transport, sensing, and metabolism of TH; among them is type 2 deiodinase, a source of 3,3′,5-triiodo-L-thyronine necessary for negative feedback on TRH neurons. Tanycytes subtypes are distinguished by position and phenotype. The end-feet of β2-tanycytes intermingle with TRH varicosities and terminals in the external layer of the ME and terminate close to the ME capillaries. Besides type 2 deiodinase, β2-tanycytes express the TRH-degrading ectoenzyme (TRH-DE); this enzyme likely controls the amount of TRH entering portal vessels. TRH-DE is rapidly upregulated by TH, contributing to TH negative feedback on HPT axis. Alterations in energy balance also regulate the expression and activity of TRH-DE in the ME, making β2-tanycytes a hub for energy-related regulation of HPT axis activity. β2-tanycytes also express TRH-R1, which mediates positive effects of TRH on TRH-DE activity and the size of β2-tanycyte end-feet contacts with the basal lamina adjacent to ME capillaries. These end-feet associations with ME capillaries, and TRH-DE activity, appear to coordinately control HPT axis activity. Thus, down-stream of neuronal control of TRH release by action potentials arrival in the external layer of the median eminence, imbricated intercellular processes may coordinate the flux of TRH into the portal capillaries. In conclusion, β2-tanycytes appear as a critical cellular element for the somatic and post-secretory control of TRH flux into portal vessels, and HPT axis regulation in mammals.

How effective is the European Union energy label? Evidence from a real-stakes experiment

Imperfect information and inattention can lead consumers to undervalue the energy efficiency of products, contributing to growing environmental problems and climate change. To nudge individuals towards energy efficiency, environmental certification schemes, such as the European Union (EU) energy label, present grade-like efficiency classes. In a real-stakes randomized controlled trial, we contrast this approach with a control condition that gives no energy-related information and an alternative treatment condition that provides detailed information on the lifetime cost of energy-using durables. Our results show that the EU energy label does not increase demand for energy-efficient products over a control condition. By contrast, lifetime-cost information increases the willingness-to-pay for energy efficiency considerably.

Is Concentrated Solar Power (CSP) a feasible option for Sub-Saharan Africa?: Investigating the techno-economic feasibility of CSP in Tanzania

There is a noticeable shortage of solar energy related information in Sub-Saharan Africa, especially about Concentrated Solar Power (CSP) technology. The Tanzanian official generation expansion plan till 2040 showed high dependency on coal and gas fired power plants and a negligible role of renewables other than large hydropower. This study investigates the techno-economic feasibility of CSP technology in Tanzania, through modelling Parabolic Trough and Solar Tower CSP technologies using the System Advisor Model developed by the U.S. National Renewable Energy Laboratory. Under debt interest rate of 7%, the levelized cost of electricity (LCOE) for the modelled Solar Tower plants ranges from 11.6 to 12.5 ¢/kWh while for the modelled Parabolic Trough plants ranges from 13.0 to 14.4 ¢/kWh. The LCOE increases significantly from 14.4 ¢/kWh (at 7% debt interest rate, for government-led projects) to 25.9 ¢/kWh (at 18% debt interest rate, for private investors-led projects). The study concludes that the feasibility of CSP in Tanzania is strongly dependent on the financing conditions. Policy mechanisms to de-risk CSP projects' investment through accessing lower debt interest rate are required to ensure the competitiveness of CSP projects in the country.