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The electrification of personal transportation holds great potential for lowering greenhouse gas emissions and reducing climate change. The promise of electric vehicles (EVs) to serve these goals has resulted in a broad range of supporting policies aimed at encouraging widespread EV adoption at both the state and federal levels in the United States and around the world. While the EV revolution and prospects of a world with ubiquitous EVs are impacting various industries and many aspects of daily life, strategic interactions between the power grid and EVs are crucial for a successful energy transition. However, managing the interplay between EVs and the power grid remains a challenge. Motivated by that tension, this paper surveys a variety of solutions, policies, and incentives that are focused on effectively managing EV charging behaviors. The paper's objective is to explore these tools to ensure that EV owners have ultimate control over their personal vehicles while simultaneously allowing the power grid to mitigate adverse network impacts. Furthermore, this paper examines the role of charging infrastructure technology and its strategic placement in facilitating the seamless integration of EVs into the grid. Additionally, the paper highlights financial mechanisms associated with EV integration and discusses the consequences of these mechanisms. [ABSTRACT FROM AUTHOR]

Small and medium commercial buildings account for nearly half of the energy consumed by commercial buildings in the United States. While energy retrofits can significantly reduce building energy consumption, buildings' owners often lack the capital and experience to perform detailed energy audits and retrofit assessments. The purpose of this paper is to introduce a low-investment, bottom-up and simplified methodology for identifying energy retrofit opportunities that benefit the owners of small and medium sized office buildings In particular, the paper addresses small and medium commercial buildings on a university campus as a proof-of-concept for other owner-operators that have small and medium commercial facilities in their portfolio. The methodology consists of an eight-step framework using publicly-available and simplified tools. While energy audits and retrofit opportunity assessments are not new, a low-cost methodology for owner-operators of small and medium commercial buildings to analyze energy consumption and identify retrofit opportunities represents a contribution to knowledge. A medium office building on a university campus in Arizona served as a case study to validate the methodology. The case study showed a maximum potential energy reduction of an estimated 50%, but the figure varies based on the types of retrofit (deep versus light), energy conservation measures selected and implemented, invested resources, and interactive effects between measures. This methodology is extensible to other owner-operators that have building utility data and would like to perform retrofit opportunity assessments themselves. [ABSTRACT FROM AUTHOR]

This paper investigates the effects of coronavirus disease 2019 (COVID-19) on the performance of arbitrage trading in the energy market using daily data covering the period between 1 January 2015 and 5 December 2021. The investigation was achieved by utilizing a parametric pairs-trading model, where pairs of energy-related securities, including futures, stocks and ETFs traded in the United States, are formed. The empirical results suggest that the out-of-sample performances of pair trading declined sharply in the face of COVID-19. Dividing the whole data sample into two sub-samples, we found that the strategy performed well before COVID-19 but yielded poor results in the pandemic era. The analysis presented in this paper could serve as a benchmark for arbitrage-based trading models in the energy market during the pandemic. [ABSTRACT FROM AUTHOR]

Investigating the determinants of environmental sustainability has become a very attractive and popular area of research in response to the United Nations' sustainable development goals (SDGs) and COP26 targets. Against this backdrop, this paper aims to explore the effects of renewable energy, technological innovation, and the insurance market on environmental sustainability in the United States (US). This work contributes to the extant body of knowledge by exploring the effect of the insurance market on the load capacity factor (LC), specifically regarding the US. The LC tracks a certain ecological threshold by simultaneously comparing biocapacity and ecological footprint (EF), thereby providing a comprehensive empirical analysis of ecological sustainability determinants. However, this proxy includes the combined attributes of the demand and supply sides of ecological sustainability. Using the recently developed autoregressive distributed lag method, this research reveals that the insurance market adversely affects the LC in the US. The overall outcomes highlight the positive role of renewable energy, technological innovation, and the insurance market in achieving the SDGs and COP26 targets. Policy recommendations for policymakers concerning promoting renewable energy, green innovation activities, the green insurance market, and ecological regulations are also discussed. [ABSTRACT FROM AUTHOR]

Building energy codes are essential tools for achieving energy efficiency in buildings. However, the full energy savings potential of these codes can only be realized if buildings are constructed in compliance with them. Therefore, evaluating building energy code compliance is crucial in bridging the gap between the energy efficiency requirements set by energy codes and the actualized energy savings achieved. An energy code compliance evaluation serves as a mechanism to assess construction practices, evaluate the effectiveness of code enforcement, identify gaps in compliance, and guide strategies for improvement through training and education. Conducting code compliance evaluation activities involves field studies that require careful design and significant resources. Historically, more emphasis has been placed on developing and adopting building energy codes, while efforts to evaluate compliance have been relatively limited and lacking consistent approaches. The passage of the 2009 American Recovery and Reinvestment Act (ARRA), which mandated that states create plans for achieving 90% compliance within eight years, stimulated the need for an energy code compliance evaluation. As a result, federal, state, and local governments, and utilities have invested in the development of methodologies and tools for code compliance evaluation studies. This paper reviews the code compliance evaluation studies conducted in the United States over the past three decades. It describes and compares the methodologies and metrics used to assess building energy code compliance, summarizes the general elements and steps involved in the evaluation process, and discusses common issues in these studies. Over time, code compliance evaluation methodologies have evolved from isolated development within individual states, regions, and utilities, to widely accepted protocols applicable across different states and local jurisdictions. There has been a transition in compliance metrics, shifting from historical compliance rates to energy-consumption-oriented approaches. [ABSTRACT FROM AUTHOR]

The authors review the efforts made in the last five years to implement Demand Response (DR) programs, considering and studying several models and countries. As motivation, climate change has been a topic widely discussed in the last decades, namely in the power and energy sectors. Therefore, it is crucial to substitute non-renewable fuels with more environment-friendly solutions. Enabling Distributed Generation (DG), namely using renewable resources such as wind and solar, can be part of the solution to reduce the greenhouse effects. However, their unpredictable behavior might result in several problems for network management. Therefore, the consumer should become more flexible towards this new paradigm where the generation no longer follows the demand requests. With this, Demand Response (DR) concept is created as part of this solution. This paper studies the European Union and United States' current status, with over 50 references. [ABSTRACT FROM AUTHOR]

The proliferation of inverter-based distributed energy resources (IBDERs) has increased the number of control variables and dynamic interactions, leading to new grid control challenges. For stability analysis and designing appropriate protection controls, it is important that IBDER models are accurate. This paper focuses on the accurate estimation and parameter calibration of DER_A, a recently proposed aggregated IBDER model. In particular, we focus on the parameters of the reactive power–voltage regulation module. We formulate the problem of parameter tuning as a non-linear least square minimization problem and solve it using the Levenberg–Marquardt (LM) method. The LM method is primarily chosen due to its flexibility in adaptively selecting between the steepest descent and Gauss–Newton methods through a damping parameter. The LM approach is used to minimize the error between the actual measurements and the estimated response of the model. Further, the computational challenges posed by the numerical calculation of the Jacobian are tackled using a quasi-Newton root-finding approach. The proposed method is validated on a real feeder model in the northeastern part of the United States. The feeder is modeled in OpenDSS and the measurements thus obtained are fed to the DER_A model for calibration. The simulation results indicate that our approach is able to successfully calibrate the relevant model parameters quickly and with high accuracy, with a total sum of square error of 3.57 × 10 − 7 . [ABSTRACT FROM AUTHOR]

A smart city is a city that binds together technology, society, and government to enable the existence of a smart economy, smart mobility, smart environment, smart living, smart people, and smart governance in order to reduce the environmental impact of cities and improve life quality. The first step to achieve a fully connected smart city is to start with smaller modules such as smart homes and smart buildings with energy management systems. Buildings are responsible for a third of the total energy consumption; moreover, heating, ventilation, and air conditioning (HVAC) systems account for more than half of the residential energy consumption in the United States. Even though connected thermostats are widely available, they are not used as intended since most people do not have the expertise to control this device to reduce energy consumption. It is commonly set according to their thermal comfort needs; therefore, unnecessary energy consumption is often caused by wasteful behaviors and the estimated energy saving is not reached. Most studies in the thermal comfort domain to date have relied on simple activity diaries to estimate metabolic rate and fixed values of clothing parameters for strategies to set the connected thermostat's setpoints because of the difficulty in tracking those variables. Therefore, this paper proposes a strategy to save energy by dynamically changing the setpoint of a connected thermostat by human activity recognition based on computer vision preserving the occupant's thermal comfort. With the use of a depth sensor in conjunction with an RGB (Red–Green–Blue) camera, a methodology is proposed to eliminate the most common challenges in computer vision: background clutter, partial occlusion, changes in scale, viewpoint, lighting, and appearance on human detection. Moreover, a Recurrent Neural Network (RNN) is implemented for human activity recognition (HAR) because of its data's sequential characteristics, in combination with physiological parameters identification to estimate a dynamic metabolic rate. Finally, a strategy for dynamic setpoints based on the metabolic rate, predicted mean vote (PMV) parameter and the air temperature is simulated using EnergyPlus™ to evaluate the energy consumption in comparison with the expected energy consumption with fixed value setpoints. This work contributes with a strategy to reduce energy consumption up to 15% in buildings with connected thermostats from the successful implementation of the proposed method. [ABSTRACT FROM AUTHOR]

Wind power is a popular renewable energy source, and the accurate prediction of wind speed plays an important role in improving the power generation efficiency of wind turbines and ensuring the normal operation of wind power equipment. Due to the instability and randomness of wind speed, it is difficult to achieve accurate prediction by traditional prediction methods. To improve the power generation efficiency of wind turbines and realize the predictability of wind speed, a hybrid wind speed prediction model based on GRUs (gated recurrent units) was constructed in this paper based on a deep neural network and feature extraction method. The hybrid model feature extraction module was implemented based on a combination of Tsfresh (a python package for time series feature extraction) and sparse PCA (sparse principal component analysis), and the network structure and other hyperparameters of the GRU module were determined through experiments. The model was validated using actual wind measurement data from a wind farm on the west coast of the United States. The results showed that the proposed model had less computational time and higher computational accuracy than the SARIMAX (seasonal auto-regressive integrated moving average with exogenous factors) and LSTM (long short-term memory) models. [ABSTRACT FROM AUTHOR]

Accurately capturing wind speed fluctuations and quantifying the uncertainties has important implications for energy planning and management. This paper proposes a novel hybrid machine learning model to solve the problem of probabilistic prediction of wind speed. The model couples the light gradient boosting machine (LGB) model with the Gaussian process regression (GPR) model, where the LGB model can provide high-precision deterministic wind speed prediction results, and the GPR model can provide reliable probabilistic prediction results. The proposed model was applied to predict wind speeds for a real wind farm in the United States. The eight contrasting models are compared in terms of deterministic prediction and probabilistic prediction, respectively. The experimental results show that the LGB-GPR model improves the point forecast accuracy (RMSE) by up to 20.0% and improves the probabilistic forecast reliability (CRPS) by up to 21.5% compared to a single GPR model. This research is of great significance for improving the reliability of wind speed, probabilistic predictions, and the sustainable development of new energy. [ABSTRACT FROM AUTHOR]

This study uses the U.S. EPA data and classification of products, which includes three main categories: durables with a lifetime over 3 years, non-durables with a lifetime below 3 years, and containers and packages, which are consumed within one year. It builds connections between the management of waste products and the energy sector, by evaluating the potential contribution of such products to the U.S. energy grid, and assessing the opportunity to substitute fossil fuels, both for electricity and residential heat production. Finally, this study conducts a vis-à-vis comparison between the U.S. and the EU progress on waste management, and the associated GHG emissions. Sankey diagrams were produced to represent the flows of products management from 1990 to 2018, and the results were assessed by considering the amounts produced, the composition, and the disposition methods used, the energy potential of waste products landfilled, and the associated greenhouse gases (GHG) emissions. The results indicate that the recycling of containers and packages have increased significantly during the 28-year period and became the dominant method of managing such products in the U.S. in 2015. Durable and non-durable products are mainly landfilled, and the situation has remained unchanged in the 2010s. Assuming that 30% of waste products landfilled in the U.S. were combusted for energy instead, it would have resulted in the substitution of <5% of fossil fuels used for electricity, but up to a 68% substitution of fossil fuels, such as propane, used for residential space and water heating. In the U.S., over 85% of GHG emissions are associated with the landfilling of waste materials, and although improvements in capturing and beneficially utilizing methane are implemented, the total GHG emissions have remained almost the same since 2015, with a tendency to increase. The European experience has shown that recycling and waste-to-energy are complementary in diverting materials from landfills, in enhancing energy security, and in significantly reducing GHG emissions from waste management. Future directions are discussed. [ABSTRACT FROM AUTHOR]

The electrical power infrastructure of the modern world is advanced, efficient, and robust, yet power outages still occur. In addition to affecting millions of people around the world, these outage events cost billions of dollars to the global economy. In this paper, the revenue loss borne by electricity-supplying companies in the United States due to power outage events is estimated and predicted. Various factors responsible for power outages are considered in order to present an exploratory data analysis at the U.S. level, followed by the top ten affected states, which bear over 85% of the total revenue loss. The loss is computed using historic observational data of electricity usage patterns and the tariff offered by the energy suppliers. The study is supplemented with reliable and publicly available records, including electricity usage patterns, the consumer category distribution, climatological annotations, population density, socio-economic indicators and land area. Machine learning techniques are used to predict the revenue loss for future outage events, as well as to characterize the key parameters for efficient prediction and their partial dependence. The results show that the revenue loss is a function of several parameters, including residential sales, percentage of industrial customer, time-period of the year, and economic indicators. This study may help energy suppliers make risk-informed decisions, while developing revenue generation strategies as well as identifying safer investment avenues for long-term returns. [ABSTRACT FROM AUTHOR]

Nuclear energy, renewable energy, and alternative energy sources are all crucial for sustainable green energy. However, the existing literature often needs to pay more attention to the role of nuclear energy in achieving sustainable development goals. This study analyzes the impact of green technological innovation, nuclear energy consumption, and trade openness on environmental quality in the US. The authors used the ARDL bounds to identify cointegration relationships, which is appropriate for this study's dataset as it works well with smaller samples. They also used the Toda–Yamamoto causality test to examine causal links. The ARDL cointegration results indicate a significant long-term relationship between CO 2 emissions, green technological innovation, nuclear energy consumption, and trade openness. Green technological innovation has a negative impact on CO 2 emissions. Higher nuclear energy consumption is associated with lower CO 2 emissions, while greater trade openness is associated with higher CO 2 emissions, although these effects are less certain. The results suggest promoting green technological innovation and nuclear energy can be effective strategies for reducing CO 2 emissions, while the impact of trade openness requires careful consideration due to its potential to increase emissions. [ABSTRACT FROM AUTHOR]

Federal and state governments in the United States (US) are promoting the transition from traditional Diesel School Buses to Electric School Buses (ESBs). This would prevent the emission of deleterious air pollutants that affect students and communities while simultaneously contributing to a reduction in greenhouse gases, aiding in the fight against climate change. However, due to their significant size and long routes, ESBs require large batteries with significant electricity demand. If this additional electricity demand is supplied to hundreds of thousands of EBSs at peak consumption times, the strain on the grid may be detrimental, while transportation costs for schools could dramatically increase. Furthermore, if EBSs are charged using traditional hydrocarbon generation, the environmental benefits of these projects may be significantly reduced. Therefore, applying renewable energy presents a host of synergistic opportunities to reduce emissions while providing inexpensive electricity to schools. Solar energy is abundant in large portions of the US, potentially providing many schools with ample inexpensive and sustainable electricity to power their transportation equipment and meet other requirements at their facilities. This research developed a novel framework to integrate publicly available big data provided by federal and state agencies in the US, as well as National Laboratories, to provide stakeholders with actionable information to develop EBS grid-to-vehicle (V2G) systems across the US. Geographic Information Systems, data analytics and Business Intelligence were applied to assess and characterize solar energy generation and consumption patterns. The novel integration of the systems in the proposed framework provided encouraging results that have practical implications for stakeholders to develop successful and sustainable ESB V2G facilities. These results identified many schools across the US that would significantly benefit from the use of solar energy and be able to supply their local communities during idle times with renewable energy through V2G. The renewable energy resource would be capable of charging ESBs at a low cost for operational availability as required. The results indicate that the proposed ESB V2G system will provide significant benefits to both schools and their local communities. The feasibility of the proposed endeavor was validated by the results of the study, providing both school and solar energy stakeholders with insights into how to better manage such a complex system. [ABSTRACT FROM AUTHOR]

In 2023, residential and commercial sectors together consumed approximately 27.6% of total United States (U.S.) energy, equivalent to about 20.6 quadrillion Btu. Factoring in the electrical system energy losses, the residential sector represented approximately 19.7% of total U.S. energy consumption during that time. There were approximately 144 million housing units in the United States in 2023, which is increasing yearly. In this study, information on energy usage in the United States residential sector has been analyzed and then represented as energy intensities to establish benchmark data and to compare energy consumption of varying sizes and locations. First, public sources were identified and data from these previously published sources were aggregated to determine the energy use of the residential sector within the US. Next, as part of this study, the energy data for seven houses/apartments from five different United States climate zones were collected firsthand. That data were analyzed, and the energy intensity of each home was calculated and then compared with the energy intensities of the other homes in the same states using Residential Energy Consumption Survey (RECS) data. The energy intensity for each facility was calculated based on the actual energy bills. Finally, the study evaluated the carbon footprint associated with residential energy consumption in all 50 states to reinforce the importance of sustainable development initiatives. [ABSTRACT FROM AUTHOR]

This study presents an efficient end-to-end (E2E) learning approach for the short-term load forecasting of hierarchically structured residential consumers based on the principles of a top-down (TD) approach. This technique employs a neural network for predicting load at lower hierarchical levels based on the aggregated one at the top. A simulation is carried out with 9 (from 2013 to 2021) years of energy consumption data of 50 houses located in the United States of America. Simulation results demonstrate that the E2E model, which uses a single model for different nodes and is based on the principles of a top-down approach, shows huge potential for improving forecasting accuracy, making it a valuable tool for grid planners. Model inputs are derived from the aggregated residential category and the specific cluster targeted for forecasting. The proposed model can accurately forecast any residential consumption cluster without requiring any hyperparameter adjustments. According to the experimental analysis, the E2E model outperformed a two-stage methodology and a benchmarked Seasonal Autoregressive Integrated Moving Average (SARIMA) and Support Vector Regression (SVR) model by a mean absolute percentage error (MAPE) of 2.27%. [ABSTRACT FROM AUTHOR]

The ambitious target of net-zero emission by 2050 has been aggressively driving the renewable energy sector in many countries. Leading the race of renewable energy sources is solar energy, the fastest growing energy source at present. The solar industry has witnessed more growth in the last decade than it has in the past 40 years, owing to its technological advancements, plummeting costs, and lucrative incentives. The United States is one of the largest producers of solar power in the world and has been a pioneer in solar adoption, with major projects across different technologies, mainly photovoltaic, concentrated solar power, and solar heating and cooling, but is expanding towards floating PV, solar combined with storage, and hybrid power plants. Although the United States has tremendous potential for exploiting solar resources, there is a scarcity of research that details the U.S. solar energy scenario. This paper provides a comprehensive review of solar energy in the U.S., highlighting the drivers of the solar industry in terms of technology, financial incentives, and strategies to overcome challenges. It also discusses the prospects of the future solar market based on extensive background research and the latest statistics. In addition, the paper categorizes the U.S. states into five tiers based on their solar prospects calculated using analytical hierarchy process and regression analysis. The price of solar technologies in the U.S. is also predicted up to 2031 using Wright's law, which projected a 77% reduction in the next decade. [ABSTRACT FROM AUTHOR]

Dependence on petroleum and petrochemical products is unsustainable; it is both a finite resource and an environmental hazard. Biodiesel has many attractive qualities, including a sustainable feedstock; however, it has its complications. The pyrolysis (a process already in common use in the petroleum industry) of biodiesel has demonstrated the formation of smaller hydrocarbons comprising many petrochemical products but experiments suffer from difficulty quantifying the myriad reaction pathways followed and products formed. A computational simulation of pyrolysis using "ab initio molecular dynamics" offers atomic-level detail of the reaction pathways and products formed. Herein, the most prevalent fatty-acid ester (methyl linoleate) from the most prevalent feedstock for biodiesel in the United States (soybean oil) is studied. Temperature acceleration within the atom-centered density matrix propagation formalism (Car–Parrinello) utilizing the D3-M06-2X/6-31+G(d,p) model chemistry is used to compose an ensemble of trajectories. The results are grounded in comparison to experimental studies through agreement in the following: (1) the extent of reactivity (40% in the experimental and 36.1% in this work), (2) the homology of hydrocarbon products formed (wt % of C6–C10 products), and (3) the CO/CO2 product ratio. Deoxygenation pathways are critically analyzed (as the presence of oxygen in biodiesel represents a disadvantage in its current use). Within this ensemble, deoxygenation was found to proceed through two subclasses: (1) spontaneous deoxygenation, following one of four possible pathways; or (2) induced deoxygenation, following one of three possible pathways. [ABSTRACT FROM AUTHOR]

Carbon Capture, Utilization, and Storage (CCUS) represents a vital technology for addressing pressing global challenges such as climate change and carbon emissions. This research aims to explore the relationship between the CCUS capability and carbon emissions in the United States considering thirteen predictors of CCUS and carbon emissions. Incorporating these predictors, we aim to offer policymakers insights to enhance CCUS capabilities and reduce carbon emissions. We utilize diverse econometric techniques: OLS, Lasso, Ridge, Elastic Net, Generalized Method of Moments, and Seemingly Unrelated Regression. Elastic Net outperforms the other models in explaining CCUS, while OLS is effective for carbon emissions. We observe positive impacts of the number of projects and foreign direct investment on the CCUS capacity, but limited influence from the CCUS technology level. However, the relationship between the CCUS capacity and carbon emissions remains limited. Our study highlights the importance of incentivizing projects to increase CCUS capabilities and recognizes the critical role of legal and regulatory frameworks in facilitating effective CCUS implementation in the US. Moreover, we emphasize that achieving decarbonization goals necessitates the development of affordable green alternatives. It is essential to view CCUS as a complementary, rather than a sole, solution for emission reduction as we work towards achieving net-zero emission targets. [ABSTRACT FROM AUTHOR]

This study investigates the impact of an active workstation on heating and cooling energy consumption in commercial buildings. The improved performance and health of office workers are well-known and supported by several surveys and studies. However, the preferred and revised indoor air temperature for different activities of the employees, such as standing and walking, directly impacts the energy consumption of the building. Therefore, this study systematically quantifies the impact of indoor environments on building energy consumption using a simulation tool. Based on literature reviews with multidisciplinary fields, revised setpoint temperature, metabolic rate, and required outdoor air rate due to the level of active workstation (standing and walking) are applied to EnergyPlus reference building models. The impact on heating and cooling energy consumption is analyzed by considering several representative cities in the United States. Their average sensitivity was found to be 7.28% lower and 14.28% higher for heating and cooling energy, respectively. The total increment of $13,887 represents approximately 0.16% of the total profit of the corporation. This increment can be justified given the benefits to workers' health, which include lower insurance costs and improved performance. This suggests that it is economically desirable to deploy active workstations widely. [ABSTRACT FROM AUTHOR]

As renewable electricity generation continues to increase in the United States (US), considerable effort goes into matching heterogeneous supply to demand at a subhour time-step. As a result, some electric providers offer incentive-based programs for residential consumers that aim to reduce electric demand during high-demand periods. There is little research into determinants of consumer response to incentive-based programs beyond typical sociodemographic characteristics. To add to this body of literature, this paper presents the findings of a dichotomous choice contingent valuation (CV) survey targeting US ratepayers' participation in a direct-load-control scheme utilizing a smart thermostat designed to reallocate consumer electricity demand on summer days when grid stress is high. Our results show approximately 50% of respondents are willing to participate at a median willingness-to-accept (WTA) figure of USD 9.50 (95% CI: 3.74, 15.25) per month that lasts for one summer (June through August)—or slightly less than USD 30 per annum. Participation is significantly affected by a respondent's attitudes and preferences surrounding various environmental and institutional perspectives, but not by sociodemographic characteristics. These findings suggest utilities designing direct-load-control programs may improve participation by designing incentives specific to customers' attitudes and preferences. [ABSTRACT FROM AUTHOR]

Heavy-duty trucks contribute approximately 20% of fuel consumption in the United States of America (USA). The fuel economy of heavy-duty vehicles (HDV) is affected by several real-world parameters like road parameters, driver behavior, weather conditions, and vehicle parameters, etc. Although modern vehicles comply with emissions regulations, potential malfunction of the engine, regular wear and tear, or other factors could affect vehicle performance. Predicting fuel consumption per trip based on dynamic on-road data can help the automotive industry to reduce the cost and time for on-road testing. Data modeling can easily help to diagnose the reason behind fuel consumption with a knowledge of input parameters. In this paper, an artificial neural network (ANN) was implemented to model fuel consumption in modern heavy-duty trucks for predicting the total and instantaneous fuel consumption of a trip based on very few key parameters, such as engine load (%), engine speed (rpm), and vehicle speed (km/h). Instantaneous fuel consumption data can help to predict patterns in fuel consumption for optimized fleet operations. In this work, the data used for modeling was collected at a frequency of 1Hz during on-road testing of modern heavy-duty vehicles (HDV) at the West Virginia University Center for Alternative Fuels Engines and Emissions (WVU CAFEE) using the portable emissions monitoring system (PEMS). The performance of the artificial neural network was evaluated using mean absolute error (MAE) and root mean square error (RMSE). The model was further evaluated with data collected from a vehicle on-road trip. The study shows that artificial neural networks performed slightly better than other machine learning techniques such as linear regression (LR), and random forest (RF), with high R-squared (R2) and lower root mean square error. [ABSTRACT FROM AUTHOR]

Energy generation has had several negative health impacts over the last few decades, mainly due to air pollution. One of the ways to decrease such impacts is to increase energy generation through renewable energy sources (RESs). These sources have important health co-benefits that need to be taken into consideration. This topic has been included in the literature, but research is scattered. The goal of this article is to show the status of the literature on this topic. We performed a systematic literature review on the health co-benefits of RES use, depicting the state of the art of this literature, some common findings, limitations, and lines for future research. It is clear from our analysis that this literature remains scarce. We found 28 studies fitting the inclusion criteria. Results can be summed as follows: (1) wind and solar power are the most studied RES sources; (2) most studies are for the United States and developing countries are largely understudied; and (3) health benefit results vary widely according to site-specific conditions. Overall, the existing studies show significant health co-benefits from RES use, which are important to consider when performing cost–benefit analysis for energy projects. This is particularly relevant for policy-makers and energy investors. [ABSTRACT FROM AUTHOR]

Electric vehicle (EV) charging infrastructure is present all over the United States, but charging prices vary greatly, both in amount and in the methods by which they are assessed. For this paper, we interpret and analyze charging price information from PlugShare, a crowd-sourced EV charging data platform. Because prices in these data exist in a semi-structured textual format, an ad hoc text mining approach is used to extract quantitative price information. Descriptive analytics of the processed dataset demonstrate how the prices of EV charging vary with charging level (Direct Current Fast Charging versus Level 2), geographic location, network provider, and location type. Our research indicates that a great deal of diversity and flexibility exists in structuring the prices of EV charging to enable incentives for shaping charging behaviors, but that it has yet to be widely standardized or utilized. Comparisons with estimates of the levelized cost of EV charging illustrate some of the challenges associated with operating and using these stations. [ABSTRACT FROM AUTHOR]

The massive blackout that occurred in August 2003 left over 50 million people in the northeastern and midwestern parts of the United States without electricity and caused billions of dollars in economic losses. This event highlighted the importance of sustainable and resilient energy infrastructure. Our study examines the impact of this blackout on the sustainability of financial markets by analyzing the liquidity and information asymmetry of U.S. stocks listed on major exchanges. Our results show that the blackout had a negative impact on the financial market's liquidity, as evidenced by a significant widening of bid–ask spreads and a decrease in the market quality index. We also find an increase in information asymmetry during the blackout period, as measured by higher realized spreads. Furthermore, our study reveals that the blackout had a border impact on global financial markets and the negative effect on liquidity persisted even after two weeks. [ABSTRACT FROM AUTHOR]

This study contributes to the body of literature on modeling and predicting gasoline demand by using nonlinear econometric techniques. For this purpose, dynamic model averaging (DMA) and Bayesian model averaging (BMA) combined with Artificial Bee Colony (ABC) are used to forecast gasoline consumption in the United States. The article's independent variables include demographic characteristics, economic activity, income, driving expenditures, automobile price, and road availability for annual data from 1960 to 2020. In the proposed model, not only may the coefficients and elasticity of a predictor of gasoline demand change over time, but other sets of predictors can also emerge at different periods. Moreover, this study aims to automate the process of picking two forgotten variables of the DMA model using the ABC model. Our findings indicate that dynamic model averaging significantly improves forecasting performance when compared to basic benchmark techniques and advanced approaches. Additionally, integrating it with an Artificial Bee Colony (ABC) may result in improved outcomes when time-varying forgetting variables are present. The findings of this research provide policymakers in the fields of energy economics and the environment with helpful tools and information. [ABSTRACT FROM AUTHOR]

The prescriptive path is the most widely used approach for commercial code compliance in the United States. Though easy to implement, prescriptive approaches do not typically discriminate between minimally compliant, high-performing and poorly performing HVAC system configurations. Hence, to meet aggressive energy and carbon reduction goals, it is clear that energy codes will need to transition from prescriptive to performance-based approaches, a transition that is riddled with several challenges. This paper discusses a new HVAC system-based performance approach (HVAC System Performance) which provides a simpler solution to HVAV system evaluation compared to whole building performance, while keeping tradeoffs limited to specific building systems. The Total System Performance Ratio (TSPR) is a metric for evaluation of overall system efficiency instead of individual component efficiency, a solution which could also eventually facilitate the transition to a 100% performance-based code structure. TSPR is a ratio that compares the annual heating and cooling load of a building to the annual energy consumed by the building's HVAC system. A calculation software tool has been developed for determining a building's TSPR. Already incorporated into the 2018 Washington State Energy Code, this approach is also being evaluated by ASHRAE Standard 90.l Project Committee and has the potential to provide a comprehensive performance-based approach for HVAC system evaluation and analysis. [ABSTRACT FROM AUTHOR]

In this article, we seek to present the synthesis of the global ethanol market in the United States (US) and Brazil by using caloric equivalence to empirically characterize the elasticity of supply and demand. We also seek to evaluate the relationship between world ethanol production and prices of agricultural commodities (wheat, rice, corn, soybeans and sugar) from 1981 to 2016 using climate-induced yield shocks as instrumental variables. The main results for the world market indicate that the production and demand for ethanol respond elastically to changes in commodity prices. The world ethanol production has no significant relationship with food prices. However, evaluation of the ethanol market and its interaction with the agricultural commodities market confirms the hypothesis that Brazilian ethanol is weakly related to the price of food. [ABSTRACT FROM AUTHOR]

This study comprehensively examines the effects of the COVID-19 pandemic on energy consumption in the United States. The purpose of the study is to quantify the effects of lockdowns and pandemic disruptions on energy consumption trends in order to inform policymakers and utilities on how to prepare for such events in the future. The study focuses on 2020 data collected by the Federal government. The effects are quantified using descriptive statistics. State-wise and sector-wise data have been presented using plots and heat maps. Related metrics like COVID case data, GDP, emissions, and expenditures were also presented. The total energy consumption fell by 7.5% in 2020. Besides Alaska, every state saw a decrease in energy, with some as high as 26%. The residential sector had the most states that saw an increase in energy, stemming from lockdowns and working from home. Similarly, petroleum consumption saw a decrease of 11.4% as a result of a decrease in travel. Biomass-related renewable energy generation fell by 23% due to decreased demand, while all other sources increased by 7.3%. Carbon dioxide emissions fell by 10.4%, methane by 2.8%, and nitric oxide by 6.7%. The overall per capita energy expenditure for the country dropped by 18.5%. There was a stronger correlation between GDP and energy consumption than between GDP and COVID case counts. The pandemic did not affect each state or sector evenly. The statistics and correlations presented here can be used in the ongoing effort to study the global impact of the pandemic and prepare for future challenges. [ABSTRACT FROM AUTHOR]

A variety of variables influence the optimal tilt angle of a PV panel, including the characteristics of the panel, the local seasonal weather variations, the number of daylight hours the panel is exposed to and the ambient temperature of the surroundings. In this study, the optimal PV tilt angle and maximum energy output of PV arrays was calculated for every county in the United States and compared against the practice of setting the PV tilt angle to be equivalent to the latitude angle of the PV geographic location. A PVWatts API, implemented through Python, was used in conjunction with the SciPy optimization package to find the optimal tilt angle for each county using a direct line search algorithm. Most counties (95.8%) showed a difference between the location latitude and the optimal tilt of more than one degree. Many counties showed a deviation of 2–6° lower than the location latitude. The variation of daylight hours had the largest influence on tilt angle and seasonal cloud cover and ambient temperature had varying levels of influence. Generally, winter cloud cover decreased the optimal tilt angle whereas high summer temperatures increased the tilt angle. [ABSTRACT FROM AUTHOR]

The transportation sector produces a large portion of greenhouse gas emissions in the United States. Meeting ambitious reductions in greenhouse gasses requires large-scale adoption of battery electric vehicles and has led to several policies and laws aimed at incentivizing their sales. While electric vehicles comprise a small percentage of the overall fleets of vehicles, the expected production of electric vehicles is soon expected to be in the millions. This will create challenges in providing an adequate charging infrastructure, as well as the ensuing management of the overall electricity demand at the grid level. In this work, a novel smart-charging protocol for battery electric vehicle charging at workplace parking structures is proposed. The Octopus Charger-based Mixed Integer Linear Programming protocol allows octopus chargers (i.e., charging stations with multiple cables) to independently schedule charging periods for their assigned vehicles. The proposed protocols can manage a parking structure demand load while reducing the number of installed charging stations. Driving patterns from the National Household Travel Survey were used to perform simulations, to verify and quantify the effectiveness of the proposed protocol. The proposed protocol resulted in improved peak load reductions for all simulated smart-charging scenarios when compared with uncontrolled charging. Critically, the assignment algorithm resulted in a number of required chargers close to the theoretical minimum. [ABSTRACT FROM AUTHOR]

Failure detection methods are of significant interest for photovoltaic (PV) site operators to help reduce gaps between expected and observed energy generation. Current approaches for field-based fault detection, however, rely on multiple data inputs and can suffer from interpretability issues. In contrast, this work offers an unsupervised statistical approach that leverages hidden Markov models (HMM) to identify failures occurring at PV sites. Using performance index data from 104 sites across the United States, individual PV-HMM models are trained and evaluated for failure detection and transition probabilities. This analysis indicates that the trained PV-HMM models have the highest probability of remaining in their current state (87.1% to 93.5%), whereas the transition probability from normal to failure (6.5%) is lower than the transition from failure to normal (12.9%) states. A comparison of these patterns using both threshold levels and operations and maintenance (O&M) tickets indicate high precision rates of PV-HMMs (median = 82.4%) across all of the sites. Although additional work is needed to assess sensitivities, the PV-HMM methodology demonstrates significant potential for real-time failure detection as well as extensions into predictive maintenance capabilities for PV. [ABSTRACT FROM AUTHOR]

While price responsiveness of residential demand for natural gas has important implications on resource planning and energy modelling, its estimates from prior studies are very diverse. Applying panel data analysis and five parametric specifications to monthly data for the lower 48 states in 1990–2019, we estimate own-price elasticities of residential demand for natural gas in the United States (US). Using results from cross-section dependence (CD) test, panel unit root tests, panel time-series estimators, and rolling-window analysis, we document: (1) the statistically significant (p-value ≤ 0.05) static own-price elasticity estimates are −0.271 to −0.486, short-run −0.238 to −0.555 and long-run −0.323 to −0.796; (2) these estimates vary by elasticity type, sample period, parametric specification, treatment of CD and assumption of partial adjustment; (3) erroneously ignoring the highly significant (p-value < 0.01) CD shrinks the size of these estimates that vary seasonally, regionally, and nonlinearly over time; and (4) residential natural gas shortage costs decline with the size of own-price elasticity estimates. These findings suggest that achieving deep decarbonization may require strategies that do not rely solely on prices, such as energy efficiency standards and demand-side-management programs. Demand response programs may prove useful for managing natural gas shortages. [ABSTRACT FROM AUTHOR]

Achieving Net zero emissions is a common challenge facing all mankind. Low-carbon electricity has always been the main research field of global GHG emission control. The current article aims to use the bibliometric analysis to describe the characteristics and trends of low-carbon electricity publications from 1983 to 2021. We found that: (1) the number of publications in this area has shown an overall increase in the past 33 years. (2) the United States is the most powerful country in this field of research. Moreover, with the exception of major developed countries, more and more emerging economies have also joined the research on low-carbon power systems. (3) co-citation analysis and literature clustering characteristics show that the knowledge base in this field is focused on the decomposition of driving factors for carbon dioxide emissions and the optimization of the operation of renewable energy (RE) in low-carbon power systems. (4) the utilization of RE is a hot topic in low-carbon power research. Through this research, global scholars can be provided with the latest overview of valuable low-carbon energy research trends. [ABSTRACT FROM AUTHOR]

Biochar produced from low-value forest biomass can provide substantial benefits to ecosystems and mitigate climate change-induced risks such as forest fires. Forest residues from restoration activities and timber harvest and biochar itself are bulky and thus incur high logistic costs, so are considered major bottlenecks for the commercialization of the biochar industry. The objectives of this study were to assess the environmental footprints and techno-economic feasibility of converting forest residues in Pacific Northwest United States into biochar pellets using portable systems followed by delivery of the final product to end-users for land application (dispersion). Two portable systems (Biochar Solutions Incorporated (BSI) and Air Curtain Burner (ACB)) were considered for biochar production. A cradle-to-grave lifecycle assessment (LCA) and a discounted cash flow analysis method were used to quantify the environmental impacts and minimum selling price (MSP) of biochar. The global warming (GW) impact of biochar production through BSI and ACB was estimated to be 306–444, and 750–1016 kgCO₂eq/tonne biochar applied to the field, respectively. The MSP of biochar produced through BSI and ACB was 1674–1909 and 528–1051 USD/tonne biochar applied to the field, respectively. Pelletizing of biochar reduced GW impacts during outbound logistics (~8–20%) but increased emissions during pelletizing (~1–9%). Results show the BSI system was a more viable option in terms of GW impact, whereas the ACB system can produce biochar with lower MSP. The results of the study conclude that the production of biochar pellets through the two portable systems and applied to fields can be both an environmentally beneficial and economically viable option. [ABSTRACT FROM AUTHOR]

Offshore sites show greater potential for wind energy utilization than most onshore sites. When planning an offshore wind power farm, the speed of offshore wind is used to estimate various operation parameters, such as the power output, extreme wind load, and fatigue load. Accurate speed prediction is crucial to the running of wind power farms and the security of smart grids. Unlike onshore wind, offshore wind has the characteristics of random, intermittent, and chaotic, which will cause the time series of wind speeds to have strong nonlinearity. It will bring greater difficulties to offshore wind speed predictions, which traditional recurrent neural networks cannot deal with for lacking in long-term dependency. An offshore wind speed prediction method is proposed by using a clockwork recurrent network (CWRNN). In a CWRNN model, the hidden layer is subdivided into several parts and each part is allocated a different clock speed. Under the mechanism, the long-term dependency of the recurrent neural network can be easily addressed, which can furthermore effectively solve the problem of strong nonlinearity in offshore speed winds. The experiments are performed by using the actual data of two different offshore sites located in the Caribbean Sea and one onshore site located in the interior of the United States, to verify the performance of the model. The results show that the prediction model achieves significant accuracy improvement. [ABSTRACT FROM AUTHOR]

Research on forecasting the seasonality and growth trend of natural gas (NG) production and consumption will help organize an analysis base for NG inspection and development, social issues, and allow industrials elements to operate effectively and reduce economic issues. In this situation, we handle a comparison structure on the application of different models in monthly NG production and consumption forecasting using the cross-correlation function and then analyze the association between exogenous variables. Moreover, the SARIMA-X model is tested for US monthly NG production and consumption prediction via the proposed method for the first time in the literature review in this study. The performance of that model has been compared with SARIMA (p, d, q) * (P, D, Q)s. The results from RMSE and MAPE indicate that the superiority of the best model. By applying this method, the US monthly NG production and consumption is forecast until 2025. The success of the proposed method allows the use of seasonality patterns. If this seasonal approach continues, the United States' NG production (16%) and consumption (24%) are expected to increase by 2025. The results of this study provide effective information for decision-makers on NG production and consumption to be credible and to determine energy planning and future sustainable energy policies. [ABSTRACT FROM AUTHOR]

Even where physical conditions appear perfectly suited for wind power production, there is significant variation in the number of turbines installed. This pattern suggests that physical conditions are a pre-requisite for, but not a determinant of, that production. This study reports the results of an analysis of the county-level correlates of wind power installations in the north–south corridor of the central United States, which contains much of the country's greatest land-based wind resources. This study focuses on the relative effects of social capital, global climate change concern, and local biodiversity, while controlling for other potential explanations that previous research has identified as leading to support for or to opposition to turbine installation. We find (1) that greater local biodiversity is associated with fewer turbine installations; (2) that the percent of the public who believe humans are causing climate change is not associated with the number of installed turbines; and (3) that a higher degree of county-level social capital is associated with fewer installations. These findings suggest the predominance of local considerations over global ones when it comes to the actual siting of turbines. [ABSTRACT FROM AUTHOR]