Utilities Research Articles

The Concept of Equality under the Indigenous and Western Legal Systems: Issues and Challenges on Sustainable Development of Africa

Communities in Africa in the pre-colonial days lived with one another in a just and equitable manner, in love, on the principles of ubuntu. The way of life was further sustained by the communities’ perception and equation of the land with humanity, where everyone had equal access to the land as factor of production. The communal system which assured of equal treatment of everyone suffered a set-back, through centuries of slave trading and the colonization and the eventual imposition of western laws on indigenous peoples of Africa, these transformed the indigenous communities from their classlessness into stratified un-equal societies of those who have and those who do not have. Corruption evolves as a result of private property ownership and this further compounds in-equality, such that communal properties are unfairly taken over by few individuals, under non-transparent privatisation of public utilities. Access to factors of production and to justice in the post-colonial Africa is a myth on account of technicality and cost. The prospect of sustaining the pre-colonial equitable access to factors of production and to justice through oral tradition suffers a setback on account of the loss of cultural archives like the African traditional religion and the indigenous languages.

Characterizing Food Policy Councils' Network Partnerships and COVID-19 Responses.

The COVID-19 pandemic pushed millions of Americans into food insecurity. Food policy councils (FPCs) across the country played a vital role in organizing coordinated food responses across multiple sectors. We used a social network analysis (SNA) approach to investigate: (1) the network of partnering organizations and agencies within FPCs; (2) how the characteristics of FPCs' network partnerships (i.e., degree, coreness, and density) related to programmatic, policy, and advocacy actions in response to the pandemic; and (3) how FPCs' use of a racial or social equity framework shifted their network partnerships and responses. Local government agencies and food supply chain actors were core in FPCs' network partnerships, while public utilities, correctional facilities, social justice groups, and others were non-core partners. Network density was more likely to be associated with any action by FPCs, and it was especially pronounced for advocacy actions taken by FPCs; trends were similar among FPCs that reported using a racial or social equity framework. The findings begin to uncover core actors in FPCs' partnerships and opportunities to establish new partnerships, particularly with social justice groups. The results also suggest that network density (interconnectedness) may be more important than other network characteristics when responding to food-related needs.

Organisational ethics context factors in a public energy utility company: A millennial view

Orientation: Millennials have become the largest age cohort in the modern world of work. Understanding the phenomenological experiences of millennials may be the key to facilitating more ethical organisational cultures.Research purpose: The aim of this article is to explore the lived, working experiences of millennials’ organisational ethics contexts factors as predisposed by ethical leadership in an organisation in the energy sector.Motivation for the study: An extensive body of research exists concerning the statistical relationship between ethical leadership, ethical culture, and workplace ethics climates. However, less explored is qualitative studies regarding millennials’ work experiences of organisational ethics context factors.Research design, approach and method: A qualitative research design was selected. Purposive and convenience sampling were utilised. The sample included eight millennials, and face-to-face interviews were used for data collection. Data were analysed by means of content analysis.Main findings: The findings suggest an ambivalent and paradoxical split in how millennials experience the ethical context factors in the participating organisation.Practical and managerial implications: The study has implications for the type of leadership styles that should be considered when engaging with millennials. Top human resource management could use the results to create a working environment that actively fosters psychological attachment, satisfaction, and boosts employee commitment.Contribution or value added: Expanding and enhancing the comprehension of how ethical leadership contributes to the theory of ethical leadership in the work context.

Assessing the Scientific Relevance of Content in Open Access Repositories

The results of monitoring 106 universities — participants of the Priority-2030 programme to determine the popularity and scientific significance of the content of domestic repositories are presented. Among them only 40 institutional repositories of universities were found, it was revealed that 20 of them are closed for a remote user. In 12 repositories, statistical information about the use of their content was found and the following indicators were searched and analyzed: number of views, downloads, citations, evaluations, mentions in social networks. It was found that domestic repositories do not use altmetrics services from commercial aggregators. Half of the repositories provide information on the number of views and downloads of internal repository counters. In six repositories, some information could be found through links to external sources where the full text of the publication is available: journal and conference websites, and scientometric databases. However, these sources, with the exception of publications in foreign periodicals, provide only internal statistics. The study of quantitative indicators of internal counters of repositories and scientometric databases gave uneven results.More complete data on the demand for some repository objects could be established on publications posted on the websites of foreign journals, since most of them use Altmetric or PlumX altmetric services that aggregate information from various sources. However, the quantitative indicators of this data show an inactive utilization of domestic publications abroad.The study reveals that altmetric metrics are poorly represented in repositories. Internal counters do not provide complete information on the demand for materials from institutional repositories, and altmetric services from commercial aggregators are hardly used. At the same time, the use of altmetric services PlumX and Altmetric provides more complete information for evaluating and adjusting the strategy of research promotion by universities.

Assessing the Scientific Relevance of Content in Open Access Repositories

The results of monitoring 106 universities — participants of the Priority-2030 programme to determine the popularity and scientific significance of the content of domestic repositories are presented. Among them only 40 institutional repositories of universities were found, it was revealed that 20 of them are closed for a remote user. In 12 repositories, statistical information about the use of their content was found and the following indicators were searched and analyzed: number of views, downloads, citations, evaluations, mentions in social networks. It was found that domestic repositories do not use altmetrics services from commercial aggregators. Half of the repositories provide information on the number of views and downloads of internal repository counters. In six repositories, some information could be found through links to external sources where the full text of the publication is available: journal and conference websites, and scientometric databases. However, these sources, with the exception of publications in foreign periodicals, provide only internal statistics. The study of quantitative indicators of internal counters of repositories and scientometric databases gave uneven results.More complete data on the demand for some repository objects could be established on publications posted on the websites of foreign journals, since most of them use Altmetric or PlumX altmetric services that aggregate information from various sources. However, the quantitative indicators of this data show an inactive utilization of domestic publications abroad.The study reveals that altmetric metrics are poorly represented in repositories. Internal counters do not provide complete information on the demand for materials from institutional repositories, and altmetric services from commercial aggregators are hardly used. At the same time, the use of altmetric services PlumX and Altmetric provides more complete information for evaluating and adjusting the strategy of research promotion by universities.

Artificial-intelligence-enabled dynamic demand response system for maximizing the use of renewable electricity in production processes

AbstractThe transition towards renewable electricity provides opportunities for manufacturing companies to save electricity costs through participating in demand response programs. End-to-end implementation of demand response systems focusing on manufacturing power consumers is still challenging due to multiple stakeholders and subsystems that generate a heterogeneous and large amount of data. This work develops an approach utilizing artificial intelligence for a demand response system that optimizes industrial consumers’ and prosumers’ production-related electricity costs according to time-variable electricity tariffs. It also proposes a semantic middleware architecture that utilizes an ontology as the semantic integration model for handling heterogeneous data models between the system’s modules. This paper reports on developing and evaluating multiple machine learning models for power generation forecasting and load prediction, and also mixed-integer linear programming as well as reinforcement learning for production optimization considering dynamic electricity pricing represented as Green Electricity Index (GEI). The experiments show that the hybrid auto-regressive long-short-term-memory model performs best for solar and convolutional neural networks for wind power generation forecasting. Random forest, k-nearest neighbors, ridge, and gradient-boosting regression models perform best in load prediction in the considered use cases. Furthermore, this research found that the reinforcement-learning-based approach can provide generic and scalable solutions for complex and dynamic production environments. Additionally, this paper presents the validation of the developed system in the German industrial environment, involving a utility company and two small to medium-sized manufacturing companies. It shows that the developed system benefits the manufacturing company that implements fine-grained process scheduling most due to its flexible rescheduling capacities.

High-End Street Vendor Economy and Sustainable Urban Development: A Comprehensive Analysis of Policy, Planning, and Social Engagement

This article explores the high-end street vending economy in China and its implications for sustainable urban development, particularly in the context of the COVID-19 pandemic. It examines the policy planning and social engagement that have facilitated the growth of this economic model, which has emerged as a vital component in alleviating economic pressure, promoting employment, and safeguarding livelihoods in urban settings. This study utilizes case studies to present the current status, potential, and developmental trends of the high-end street vending economy, highlighting its popularity and governmental support in cities such as Shanghai. Additionally, the research delves into the challenges and opportunities presented by this economic model, including environmental impacts, resource sustainability, social inclusivity, and economic sustainability. It provides a comprehensive analysis of how high-end street vending can contribute to urban planning, public space utilization, and the promotion of cultural diversity and urban image. Finally, the paper concludes with policy recommendations and a future outlook on the integration of high-end street vending economies within sustainable urban development frameworks.

Management of state property of the Agro-industrial complex in the Republic of Belarus

The purpose of the work was to analyze the management of state property of the Agro-industrial complex in the Republic of Belarus. In the Republic of Belarus, a decentralized (sectoral) system of ownership supervision has been formed, when shares are managed by ministries, concerns, and committees that were in charge of state-owned enterprises before their transformation into joint-stock companies. In addition to the sectoral system, there are centralized and dual systems, which are actively used by our key foreign trade partners such as the Russian Federation and the People’s Republic of China. Currently, in the Agro-industrial complex of the Republic of Belarus there is a high share of state-owned enterprises and organizations with a state share in the authorized capital. An analysis of the state share of ownership in the authorized capital of OJSC shows that in agricultural organizations in recent years it has consistently been in the range of 70–80 %, and in industrial and service organizations – 40–50 %. At the same time, there is a need to ensure an increase in the operating effi ciency of many OJSC, including by improving management tools for enterprises and organizations, including owner supervision. The article substantiates promising directions for improving owner’s supervision, including: organizational –the transition from a sectoral to a centralized model of organizing owner’s supervision in the Agro-industrial complex; economic – development of the corporate governance system in business companies; administrative and economic – development of a methodology for assessing the eff ectiveness of state property management in the Agro-industrial complex. The main methodological approaches to assessing the economic effi ciency of state property management in the Agro-industrial complex of the Republic are highlighted: 1) based on quantitative or qualitative indicators; 2) focused on the object or subject of management; taking into account only economic indicators or based on indicators of social effi ciency and public utility. The use of the results of the proposed research is aimed at improving owner supervision and will improve the efficiency of functioning of business entities of the Agro-industrial complex of the Republic of Belarus.

A novel system of liquid air energy storage with LNG cold energy and industrial waste heat: Thermodynamic and economic analysis

Liquid air energy storage (LAES) is a promising technology for large-scale energy storage applications, particularly for integrating renewable energy sources. While standalone LAES systems typically exhibit an efficiency of approximately 50 %, research has been conducted to utilize the cold energy of liquefied natural gas (LNG) gasification. This approach, applied during the LAES energy storage phase of air compression and liquefaction, can reduce the power consumption of the compressor and enhance the performance of the integrated system. However, the improvement in the system's efficiency is constrained due to the low temperature of the air entering the turbine expansion during the energy release phase. Most studies only focus on the utilization of LNG cold energy, neglecting the proper utilization of the cold energy generated during the liquid air gasification process. This paper proposes an integrated system of LNG cold energy utilization and cement industry waste heat recovery (WHR) with LAES (LNG-LAES-WHR). A thermodynamic model is constructed, and the system is optimized by using four-stage compression, four-stage expansion, and R601 as the working fluid for WHR. The optimized integrated system has a round-trip efficiency (RTE) of 176.27 % and achieves a power output of 337.53 MW. Compared to the previously proposed system, this system has a high exergy efficiency of 74.49 % with a maximum energy storage capacity of 0.5918 kW/kg LNG. The economic analysis shows that the system has the best economic performance when constructed in Zhuhai: the dynamic payback period (DPP) is 4.2 years and the life cycle net present value (NPV) is 1170.72 million USD. This system realizes the cascaded utilization of energy, both in terms of heat and cold, which is safe, flexible, and highly efficient. This work provides a reference for the design of integrated systems for large-scale LAES for industrial applications.

First validation of high-resolution satellite-derived methane emissions from an active gas leak in the UK

Abstract. Atmospheric methane (CH4) is the second-most-important anthropogenic greenhouse gas and has a 20-year global warming potential 82 times greater than carbon dioxide (CO2). Anthropogenic sources account for ∼ 60 % of global CH4 emissions, of which 20 % come from oil and gas exploration, production and distribution. High-resolution satellite-based imaging spectrometers are becoming important tools for detecting and monitoring CH4 point source emissions, aiding mitigation. However, validation of these satellite measurements, such as those from the commercial GHGSat satellite constellation, has so far not been documented for active leaks. Here we present the monitoring and quantification, by GHGSat's satellites, of the CH4 emissions from an active gas leak from a downstream natural gas distribution pipeline near Cheltenham, UK, in the spring and summer of 2023 and provide the first validation of the satellite-derived emission estimates using surface-based mobile greenhouse gas surveys. We also use a Lagrangian transport model, the UK Met Office's Numerical Atmospheric-dispersion Modelling Environment (NAME), to estimate the flux from both satellite- and ground-based observation methods and assess the leak's contribution to observed concentrations at a local tall tower site (30 km away). We find GHGSat's emission estimates to be in broad agreement with those made from the in situ measurements. During the study period (March–June 2023) GHGSat's emission estimates are 236–1357 kg CH4 h−1, whereas the mobile surface measurements are 634–846 kg CH4 h−1. The large variability is likely down to variations in flow through the pipe and engineering works across the 11-week period. Modelled flux estimates in NAME are 181–1243 kg CH4 h−1, which are lower than the satellite- and mobile-survey-derived fluxes but are within the uncertainty. After detecting the leak in March 2023, the local utility company was contacted, and the leak was fixed by mid-June 2023. Our results demonstrate that GHGSat's observations can produce flux estimates that broadly agree with surface-based mobile measurements. Validating the accuracy of the information provided by targeted, high-resolution satellite monitoring shows how it can play an important role in identifying emission sources, including unplanned fugitive releases that are inherently challenging to identify, track, and estimate their impact and duration. Rapid, widespread access to such data to inform local action to address fugitive emission sources across the oil and gas supply chain could play a significant role in reducing anthropogenic contributions to climate change.

Long-term Load Forecasting for Optimal Power System Planning and Decision-Making

Forecasting the future load growth of an area based on its load demand is often a proactive measure to ensure a steady electricity power supply to that area. The study focused on long-term load forecasting for power system planning, specifically examining the electric load demand from consumers on distribution transformers within Port Harcourt City, located in Rivers State, Nigeria. The study encompassed a comprehensive review of both statistical and artificial intelligence-based approaches. Historical load data for distribution transformer readings spanning 2008 to 2017 were acquired from the Port Harcourt Electricity Distribution Company (PHEDC) and subjected to analysis using the curve-fitting technique. For the period between 2015 and 2030, a yearly load forecast simulation was conducted using the Fourier Series model, implemented with MATLAB software. This simulation aimed to provide insights into future load demand, facilitating careful and informed decision-making in the investment, operation, and maintenance of power system equipment. The effectiveness of the forecasting investigation was assessed using the Root Mean Square Error (RMSE), confirming the efficiency, reliability, and validity of the employed model. The study's forecasted results are presented as a valuable guide and practical tool for policymakers and the utility company (PHEDC) to enhance proper planning and decision-making processes. Considering the observed trend in the results, it is suggested that installing additional transformer units in the region would be necessary to alleviate the loads on existing overloaded transformer units within the power system network.

A Novel Statistical Framework for Optimal Sizing of Grid-Connected Photovoltaic–Battery Systems for Peak Demand Reduction to Flatten Daily Load Profiles

Integrating photovoltaic (PV) systems plays a pivotal role in the global shift toward renewable energy, offering significant environmental benefits. However, the PV installation should provide financial benefits for the utilities. Considering that the utility companies often incur costs for both energy and peak demand, PV installations should aim to reduce both energy and peak demand charges. Although PV systems can reduce energy needs during the day, their effectiveness in reducing peak demand, particularly in the early morning and late evening, is limited, as PV generation is zero or negligible at those times. To address this limitation, battery storage systems are utilized for storing energy during off-peak hours and releasing it during peak times. However, finding the optimal size of PV and the accompanying battery remains a challenge. While valuable optimization models have been developed to determine the optimal size of PV–battery systems, a certain gap remains where peak demand reduction has not been sufficiently addressed in the optimization process. Recognizing this gap, this study proposes a novel statistical model to optimize PV–battery system size for peak demand reduction. The model aims to flatten 95% of daily peak demands up to a certain demand threshold, ensuring consistent energy supply and financial benefit for utility companies. A straightforward and effective search methodology is employed to determine the optimal system sizes. Additionally, the model’s effectiveness is rigorously tested through a modified Monte Carlo simulation coupled with time series clustering to generate various scenarios to assess performance under different conditions. The results indicate that the optimal PV–battery system successfully flattens 95% of daily peak demand with a selected threshold of 2000 kW, yielding a financial benefit of USD 812,648 over 20 years.

Income inequality effect of public utility infrastructure: Evidence from rural China

The income inequality effect of infrastructure investment is under-researched despite the fact that the world has been confronted with the formidable challenge of worsening income distribution. This paper addresses this gap by: (i) highlighting deficiencies in conventional inequality modeling, (ii) proposing an empirical framework that entails estimation of both income growth and income inequality effect of infrastructure using one model, and (iii) applying this model to individual-level data from rural China to estimate income growth and income inequality effect of specific public utility infrastructures. Our findings reveal that the usage of telephone and tap water infrastructure fosters rural income growth, particularly benefiting lower-income individuals, indicating benign income inequality effect. These effects are particularly notable in inland China. An investigation into underlying mechanisms shows that public utility infrastructures help improve educational attainment and health status in lower-income rural households, stimulating growth in income and reducing income inequality. These findings imply that development of public utility infrastructure can serve as a powerful policy instrument for government interventions to reduce income inequality and promote growth, especially in developing countries.

Regulatory control over the electricity market in Ukraine: the problem of duplication of powers

The purpose of this article is to substantiate the existence of the problem of duplication of powers in the system of regulatory control over the electricity market in Ukraine and to determine ways to solve it. This research task should be carried out using, first of all, the formal-dogmatic method within the framework of a critical assessment of the relevant legislative provisions, perceiving them being detached from the environment of their application, as well as relying on the method of legal and institutional modeling to outline the organizational and legal contours of the system of regulatory control over the electricity market in Ukraine. Results: the subject matter of regulatory control (supervision) over the electric power industry carried out by the State Energy Supervision Inspectorate of Ukraine covers the production, transmission, distribution of electric energy, energy storage, as well as the use of energy for their own needs by market participants in part of technical operation of power plants and networks, energy equipment, testing and repair of power plants and networks, performance of works on the design of power plants and networks, while the National Energy and Public Utilities Regulatory Commission carries out regulatory control over compliance by economic entities carrying out activities in the fields of energy with legislation in this area as well as with the licensing conditions by conducting scheduled and unscheduled on-site, as well as off-site inspections in accordance with the control procedures approved by this commission. Conclusions: the optimal institutional reform designed to ensure the exclusivity and indivisibility of powers regarding regulatory supervision (control) over the electric power market appears to be the concentration of these powers in the field of competence of the National Energy and Public Utilities Regulatory Commission as an independent market regulator with a complete and self-sufficient complex of control and supervision measures, which can be enriched with the prepared proposals on reforming the procedures of activity of the State Energy Supervision Inspectorate of Ukraine.

Surveillance of SARS-CoV-2 in wastewater by quantitative PCR and digital PCR: a case study in Shijiazhuang city, Hebei province, China

ABSTRACT In this study, we reported the first long-term monitoring of SARS-CoV-2 in wastewater in Mainland China from November 2021 to October 2023. The city of Shijiazhuang was employed for this case study. We developed a triple reverse transcription droplet digital PCR (RT-ddPCR) method using triple primer-probes for simultaneous detection of the N1 gene, E gene, and Pepper mild mottle virus (PMMoV) to achieve accurate quantification of SARS-CoV-2 RNA in wastewater. Both the RT-ddPCR method and the commercial multiplex reverse transcription quantitative polymerase chain reaction (RT-qPCR) method were implemented for the detection of SARS-CoV-2 in wastewater in Shijiazhuang City over a 24-month period. Results showed that SARS-CoV-2 was detected for the first time in the wastewater of Shijiazhuang City on 10 November 2022. The peak of COVID-19 cases occurred in the middle of December 2022, when the concentration of SARS-CoV-2 in the wastewater was highest. The trend of virus concentration increases and decreases forming a “long-tailed” shape in the COVID-19 outbreak and recession cycle. The results indicated that both multiplex RT-ddPCR and RT-qPCR are effective in detecting SARS-CoV-2 in wastewater, but RT-ddPCR is capable of detecting low concentrations of SARS-CoV-2 in wastewater which is more efficient. The SARS-CoV-2 abundance in wastewater is correlated to clinical data, outlining the public health utility of this work. Highlights First long-term monitoring of SARS-CoV-2 in wastewater in Mainland China COVID-19 outbreak was tracked in Shijiazhuang City from outbreak to containment Wastewater was monitored simultaneously using RT-ddPCR and RT-qPCR methods Triple primer-probe RT-ddPCR detects N1 and E genes of SARS-CoV-2 and PMMoV

Transforming Texas-style commodification: Sovereignty, resilience, and energy justice

ABSTRACT We examine the 2021 collapse of the Texas electrical grid as a case study of vulnerabilities introduced by the commodification of energy systems. While theories of commodification do predict several of the observed impacts of this experiment in privatization and deregulation of public utilities, we attribute these impacts to a tendentious conception of “Texas-style” sovereignty that is not captured by existing accounts of energy justice. By supplementing existing conceptions of energy justice with an evolved conception of sovereignty, we hope to provide a more capacious and defensible normative foundation for energy systems than is available elsewhere, and one capable of diagnosing and avoiding the trappings of Texas-style energy ideals.

Kinetics and structure analysis of CO2 mineralization for recycled concrete aggregate (RCA)

CO2 mineralization of solid waste in the construction industry for green concrete production is a promising approach for economically storing CO2 and recycling solid waste. Recent investigations have underscored the significance of CO2 sequestration and the augmentation of RCA performance engendered by the holistic reaction processes. While there is a pronounced interest in the optimization of macroscopic reaction conditions (e.g., temperature, pressure, etc.), the exploration and enhancement of the reaction kinetics stage often remains under explored. This study employed a reaction kinetics model to quantify the reaction rate unveiled two distinct reaction stages within the mineralization reaction. Early evaporation and pore formation established the groundwork for the diffusion mechanism of gas product layers in the middle and later stages, emphasizing the crucial role of water in connecting solid ion leaching and gas dissolution-diffusion in the reaction. The water content of the reaction has an optimal value (4%–6%), smaller particle size correlates with a higher optimal water content. Pre-soaking with a calcium hydroxide (CH) solution or suspension was employed to improve mineralization reaction kinetics, resulting in RCA with improved pore characteristics and strength. The crushing index decreased by 28.8% and 21.8%, the water absorption rate decreased by 11.1% for both coarse and fine RCA, and the cumulative total porosity of fine RCA decreased by 17.6%. This research aims to broaden the applications of waste concrete in the CO2 capture, utilization and storage (CCUS) industry and produce low-cost, extensively processed, and high-strength RCA products while contributing to sustainable CO2 management.

Applause, puzzlement, worry

AbstractIn this commentary, I argue that Leon Wansleben’s focus on financial plumbing as a source of central banks’ epistemic and instrumental power will be met by the profession with a mixture of relief, incredulity, and worry. More importantly, I maintain that central bankers’ relationship with finance varies according to whether or not they are independent from elected government, an under researched area. All this works as a point of departure for remarks, drawing on my own memories, on central banking’s relationship with neoliberalism in monetary policy, monetary operations, and banking. Finally, I urge that Wansleben’s method be applied to anti-trust and the micro-economic regulation of utility services.

DBSCAN-based energy users clustering for performance enhancement of deep learning model

Background: Due to rapid progress in the fields of artificial intelligence, machine learning and deep learning, the power grids are transforming into Smart Grids (SG) which are versatile, reliable, intelligent and stable. The power consumption of the energy users is varying throughout the day as well as in different days of the week. Power consumption forecasting is of vital importance for the sustainable management and operation of SG. Methodology: In this work, the aim is to apply clustering for dividing a smart residential community into several group of similar profile energy user, which will be effective for developing and training representative deep neural network (DNN) models for power load forecasting of users in respective groups. The DNN models is composed of convolutional neural network (CNN) followed by LSTM layers for feature extraction and sequence learning respectively. The DNN For experimentation, the Smart Grid Smart City (SGSC) project database is used and its energy users are grouped into various clusters. Results: The residential community is divided into four groups of customers based on the chosen criterion where Group 1, 2, 3 and 4 contains 14 percent, 22 percent, 19 percent and 45 percent users respectively. Almost half of the population (45 percent) of the considered residential community exhibits less than 23 outliers in their electricity consumption patterns. The rest of the population is divided into three groups, where specialized deep learning models developed and trained for respective groups are able to achieve higher forecasting accuracy. The results of our proposed approach will assist researchers and utility companies by requiring fewer specialized deep-learning models for accurate forecasting of users who belong to various groups of similar-profile energy consumption.

Pollution control bonds and overinvestment in utility companies: Does ownership matter?

Pollution control bonds and overinvestment in utility companies: Does ownership matter?