Commercial Electricity Consumption Research Articles

Predicting and decarbonizing carbon emissions from building energy use in Hong Kong: A LASSO regression approach

This study examines the total carbon emissions of Hong Kong, which ranked 74th among 218 countries in 2021, with a focus on the contribution of building energy use. Using data from open sources at ourworldindata.org and data.gov.hk from 2001 to 2020, a Least Absolute Shrinkage and Selection Operator (LASSO) regression model is developed to predict the trajectory of carbon emissions. The significance of regression coefficients is tested using bootstrap confidence intervals, and jackknife cross-validation is employed to evaluate the model's performance. The study identifies nine significant predictors, including commercial electricity consumption, private office area, and private commercial building area, among others. The findings indicate that the current carbon reduction trend is insufficient to meet the target in 2030. To achieve carbon neutrality by 2050, the study proposes a decarbonization roadmap involving a 30–40 % reduction in commercial building electricity consumption and a decreased carbon emission factor. The study underscores the importance of strategic energy management for air conditioning systems and the promotion of green building projects in effectively decarbonizing Hong Kong.

Interface design for residential energy feedback, in the Indian context

Global access to electricity has increased from 78.2% to 2000 to 90.5% in 2020, resulting in an increased electricity demand worldwide. Unlike commercial electricity consumption, which is managed by professionals, residential consumption is managed by the householders, who often lack insight into their energy usage. Quality feedback, including detailed energy consumption and tips, can lead to substantial household savings. There are several mediums for providing energy feedback, such as Short Message Service (SMS), postal letter, email, mobile app, and In-Home Display (IHD). Studies suggest that feedback through electronic media can save up to 20% of energy consumption. In this work, we aim to design mobile application interfaces that can maximize energy savings through effective feedback. The level of savings realized is dependent on the user’s preferences and understanding of the information presented. User preferences are subjective of their profile (e.g., age, occupation, income) and the cultural context (e.g., country). The possibility of energy reduction is high when the provided information matches the user preferred information for feedback. Smart homes have recently been included as an annexure in India’s building energy code (Eco Niwas Samhita 2021), indicating a growing demand for quality energy feedback in India. However, there is a lack of research that addresses what feedback information is suitable for Indian users. We conducted two questionnaire-based surveys, one to understand users’ preferences for feedback information and another to validate the designed mobile application interface screens. The surveys were conducted on two age groups, young and middle-aged adults. A Chi-Square Test of Independence was performed to assess the relationship between the user’s preference for feedback information and their age group. Participants identified total energy consumption, appliance level disaggregated information, energy-saving tips, goals, and historical consumption comparisons as the top five information types. In contrast, normative comparison was the least preferred information. The follow-up design validations suggest that the interface should be customizable to accommodate the varying preferences of users. The current findings will help customize the energy feedback display UI design as per the Indian population.Graphical

Modeling the impacts of climate change on monthly commercial sector electricity consumption: A case of Suzhou, China

AbstractMultiple regression equations are the most common method for modeling the influences of climate variables on electricity demand. However, the multiple regression equations based on time series data often suffer from the problem of serial correlation or heteroskedasticity, resulting in biased estimation of the coefficients of parameters. Given that previous studies have paid less attention to such issues, in quantifying the effects of climate change on monthly commercial electricity consumption, in Suzhou, China, we paid more attention to the detection of the autocorrelation or heteroskedasticity that exists in a regression model. The results confirm that if a regression equation suffers from autocorrelation or heteroskedasticity, taking the necessary remedial measures can significantly improve the goodness of fit for the model. Again, it also found that if the temperature is less than the lower limit of the comfort zone, the relationship between climate change and electricity consumption is linear; if the temperature exceeds the upper limit of the comfort zone, this relationship is nonlinear, and a quadratic curve is more appropriate for modeling this relationship.

Commercial consumers pay attention to marginal prices or average prices? Implications for energy conservation policies

Any energy conservation policy intervention associated with energy price variation needs to consider whether consumers respond to marginal or average pricing. To the best of our knowledge, there is no such evidence for commercial electricity consumers (such as office buildings and malls), although commercial consumers are responsible for nearly 18% of electricity consumption in the United States. This study examines a four-tiered decreasing-block pricing schedule for commercial consumers. Based on individual-consumer-level data, we analyze the daily electricity consumption of 597 commercial accounts in Phoenix metropolitan, Arizona, from May 1, 2013, to December 31, 2016. We run 2SLS models with policy-induced price variation as instrumental variables to estimate the effects of marginal and average prices on commercial electricity consumption at each cutoff point. We also study the heterogenous response across industry sectors. Our analysis shows that commercial consumers respond to both marginal and average prices, but have different responses with respect to how much electricity they consume. Higher-usage consumers tend to respond more to average prices, whereas lower-usage consumers are more sensitive to marginal prices. Our findings indicate that conservation policies should be tailored differently for commercial electricity consumers. Nonlinear electricity pricing structures can reduce energy consumption, particularly for commercial consumers who have lower energy demand if the pricing structure shifts from decreasing to increasing blocks. In contrast, a flat rate with a higher price level can limit the electricity consumption of high-use consumers. As for an industry-wise policy design, nonlinear pricing can be effectively used to reduce aggregate consumption in the construction, manufacturing, real estate, and rental and leasing industries, professional, scientific, and technical services, other services (except public administration), and public administration industries.

Impact of Electric Vehicle Adoption on Electricity Consumption and Generation: Evidence from California

The market share of electric vehicles (EV) is growing in the USA, and there are substantial numbers of federal, and state-level incentives for EV consumers. These incentives are in place primarily due to environmental concerns. This study focuses on two interrelated aspects of EV adoption. First, using monthly county-level data from 2010 to 2019, this study reveals that electric vehicles and their supportive infrastructures, such as charging stations, have a significant effect on residential and commercial electricity consumption in California. Second, analyzing electricity generation information by county, this study finds a significant negative relation between electricity usage and the share of electricity that comes from renewable sources. Although EVs emit lower pollutants than conventional vehicles, they require a significant amount of electricity for charging. If the electricity generation doesn’t involve renewable or cleaner sources, public spending on EV may not contribute to a cleaner environment as much as expected.

Impact of sectoral decompositions of electricity consumption on economic growth in India: evidence from SVAR framework.

The study examines the effects of electricity consumption from different sectors such as agricultural, commercial, domestic, industrial (HV), industrial (LV-MV) and miscellaneous sectors on economic growth over the period of 1981-2019 in the case of India. We used SVAR framework and concluded that the consumption of electricity from agriculture sector has a negative impact on economic growth, whereas the industrial (HV and MV-LV) and commercial electricity consumption has positive impact on economic growth. Similarly, electricity consumption by the domestic sector has less positive effect on economic growth. Further, we computed the total factor productivity growth (TFP) by using the DEA method and showed the effects of sector-wise electricity consumption on TFP as the robustness of our analysis. We obtain similar kind of results. From the policy perceptive, the study suggests that the government must speed up the construction of a power grid to improve the availability of electricity for achieving higher rate of economic growth.

Assessment of Power System Sustainability and Compromises between the Development Goals

Ensuring the sustainability of the European power system is one of the key priorities in the implementation of the EU’s ambitious plans to become climate-neutral by 2050. The uniqueness of the power systems of the EU member states necessitates their assessment and comparison. The article offers a composite indicator, namely, the power system sustainability index (PSS index), to assess the current level of the development of the power systems via three dimensions (social, economic, and environmental) and eight local indicators: the household electricity consumption per capita; the commercial electricity consumption per GDP; the external dependency of the power system; the energy efficiency of the generation; the capacity utilization factor; the share of organic fossil fuels; the share of renewable energy resources; and the greenhouse gas (GHG) emissions per unit of primary energy source. The “energy mix” is defined as the key impact factor, which has a contradictory effect on the local power system sustainability (LPSS) indicators, which can be represented as a set of regression models. The data of the regression analysis can be used for performing a multiobjective optimization by the local indicators, and they can determine the vectors of change required to ensure the sustainability of the power system. The research results prove that it is possible to minimize the GHG emissions per unit of primary energy source and maximize the energy efficiency of generation, while reducing the capacity utilization and increasing the external dependency of the power system.

Impacts and savings of energy efficiency measures: A case for Mexico's electrical grid

In the efforts to decarbonize the electricity sector, energy efficiency approaches have consistently shown co-benefits that merit investment and further development. Tools that can help decision-makers evaluate the economic and environmental impacts could accelerate the adoption of energy efficiency measures and support the development of targeted incentives, both spatial and temporally. In this work, a unified, corrected, and high-resolution electricity consumption framework is first developed to produce new data insights with an increased spatial and temporal resolution, allowing the possibility of performing detailed energy policy scenarios. A case scenario for Mexico is constructed, where the impacts in the electrical grid from a suite of energy efficiency measures are quantified at high spatial and temporal resolution. The novel data sets are used to model electricity-reduction approaches in great detail for the most electricity-consuming economic activities in the country. It is shown that commercial electricity consumption is underestimated by up to 3 × in government reports and that US$ 11B could be saved in the wholesale electricity market —equating to 142 MtCO2eq of avoided emissions— if a 12% electricity reduction target is implemented to the top-20 most demanding economic activities over the years 2021–2035. Potential priorities for incentives are identified based on criteria suited for different stakeholders, including economic and environmental justice dimensions.

Machine Learning Applications in Fleet Electrification: Optimizing Vehicle Maintenance and Energy Consumption

The electric vehicle (EV) market has been growing not only for passenger vehicles, where we witness high adoption of these cleaner cars, divided into Hybrid Electric Vehicles (HEVs), Plug-In Electric Vehicles (PHEVs), and Battery Electric Vehicles (BEVs), but also in buses, trucks, and vans, with an increasing conversion pressure due to regulations. In last-mile delivery, powerful optimization tools combining machine learning with operations research have become the subject of several studies, aiming to achieve efficient energy consumption in EVs by predicting drivers’ behaviors or track charts. Moreover, new strategies have been developed for something known as fleet electrification, i.e., when a carrier company aiming mainly to decrease its environmental footprint combines the operation of conventional fuel-powered vehicles with electric ones; this is not a complete switchover, but an intermediate stage. Since computational methods converged, fleet electrification attracted researchers’ interest as a techno-economical optimization question related to three main research lines with a strong potential impact on environmental electricity: energy mix-related carbon emissions. The first category of services that impacted fleet electrification before the advent of machine learning was related to condition-based maintenance. As commercial gas, electricity, heating oil, and water consumption can be budgeted across a year’s worth of monthly payments, fixing a price through the year could help these fleets include commercial formulations that account for released corporate emissions through approved carbon offsetting. In concrete numbers, our technical paper has two key goals: firstly, we investigate whether and to what extent the Vehicle Misuse Factor, as defined, and the initial state of relevant vehicle parts (tires, gearbox, motor, etc.) affect the electric energy consumption of EVs; secondly, we propose and solve the cost minimization model arising from this energy consumption prediction.

How price responsive is commercial electricity demand in the US?

We use monthly data for the lower 48 states in the US and 30-year period of 1991–2020 to estimate commercial electricity demand’s price responsiveness. Our key findings are: (1) the US commercial electricity demand’s statistically significant (p-value ≤ 0.05) own-price elasticity estimates are between − 0.03 and − 0.08; and (2) the size of these estimates has been slowly declining over time. Hence, price-induced conservation is unlikely effective, at least in the immediate future, to materially reduce the US commercial electricity consumption, thus justifying continuation of energy efficiency standards and demand side management programs.

Treatment of palm oil mill effluent using electrocoagulation powered by direct photovoltaic solar system

High commercial electricity consumption is one of the disadvantages in the operation of lengthy electrocoagulation processes. To cater to this problem, this study develops an integrated photovoltaic-electrocoagulation system in treating oil palm mill effluent (POME). This system has successfully reduced 23,837 mg/L of chemical oxygen demand (COD) and 15,153 mg/L of biological oxygen demand (BOD) in 8 h. It was found that the higher solar radiation harvested by photovoltaics produces a higher current intensity, which in turn generates more in-situ coagulants into the wastewater. This relates to COD and BOD removal’s significance from 150 to 390 min, where the current intensities are in the maximum range (between 153–181 mA). The first-order kinetic models of COD and BOD are in good correlation coefficient, which is 0.9873 and 0.9837, respectively. Overall, this study findings recommend the possibility of sustainable operation in the actual wastewater pond.

EFFECTS OF CLIMATE CHANGE ON ELECTRICITY CONSUMPTION: A DECOMPOSITION OF INDUSTRIAL, RESIDENTIAL, AGRICULTURAL, AND COMMERCIAL SECTORS

This study explores the impacts of temperature changes on electricity consumption in South Korea. In particular, the study estimates the impacts by separating them into the residential, commercial, industrial, and agricultural sectors. The study found that temperature changes affect the electricity consumption of all four sectors. However, the size and shape of impacts vary depending on the sector. While the commercial and industrial electricity consumption increase both in hot and cold temperatures, residential consumption increases only in hot temperatures. The agricultural sector shows that its consumption responds to cold temperatures more significantly than hot temperatures. These results suggest that the sectoral variations should be considered when predicting future electricity consumption. With an assumption of 2°C average temperature increases, the electricity consumption of residential, commercial, industrial, and agricultural sectors is projected to increase by 6%, 8%, 1%, and 4% in the summer season (July to September), respectively. In the spatial analysis, the annual consumption of residential and commercial sectors is projected to increase in most counties while the agricultural sector shows the opposite result. With 2°C and 5°C average temperature increases, the study found that total electricity consumption in August would increase by 4.1% (1.5[Formula: see text]TWh) and 11.3% (4.1[Formula: see text]TWh). This would create additional costs of $0.17 billion and $0.45 billion with an assumption that the average marginal cost of electricity in South Korea was 11 cents per kWh.

Inequitable and heterogeneous impacts on electricity consumption from COVID-19 mitigation measures

SummaryThe COVID-19 pandemic has exacerbated energy insecurity and economic hardship among vulnerable populations. This paper provides robust empirical evidence of the degree to which COVID-19 mitigation measures, especially the mandates of school closure and limiting business operations, have impacted electricity consumption behavior in low-income and ethnic minority groups in the United States. We use a regression discontinuity design applied to individual-consumer-level high-frequency smart meter data in Arizona and Illinois to highlight the disparities in mitigation measure impacts. We find that the mandates of school closures and limiting business operations increase residential electricity consumption by 4–5%, but reduce commercial electricity consumption by 5–8%. Considerable heterogeneity is observed across income and race: low-income and ethnic-minority populations experience a larger electricity consumption increase, reflecting the disproportionate impact of COVID-19 on electricity insecurity in the residential sector. Policies that address energy insecurity, especially during the pandemic, become essentially important.

Performance simulation of semi-transparent photovoltaic glass on a skylight for commercial building

The demand for energy in buildings is a worldwide research subject due to its importance in the global electric load share. Besides, photovoltaic conversion to generate electricity locally is one of the ways to meet that demand. This work aims to evaluate the application of semi-transparent photovoltaic glass on a skylight of a commercial building and estimate electricity consumption and production using computational simulation with EnergyPlus. The opening size was set as a variable parameter. Its performance was compared to an ordinary skylight and opaque modules under the same conditions for three different Brazilian bioclimatic zones. Results show that the area's change provided significant differences in generation and less important ones in consumption. Among the bioclimatic zones, the building presented the lowest consumption and the highest generation for all configurations in the coldest zone, making it the best region for net electricity, i.e., purchased from a utility. Comparing semi-transparent with opaque modules, the latter produced much more energy, but consumption was reduced by an average of 28% in favour of the photovoltaic glass. The main conclusion is that the use of photovoltaic technology in a semi-transparent glass is promising regarding the integration of generators to the building, but the efficiency rates need to increase to bring it closer to opaque modules in performance.

Future western U.S. building electricity consumption in response to climate and population drivers: A comparative study of the impact of model structure

Projections of building electricity consumption are used in multiple fields and for a variety of purposes, from energy utility investment decisions to global climate assessments. Existing approaches to modeling building electricity consumption span a range of structural methodologies, spatial resolutions, and temporal scales, potentially leading to divergent projections. This paper compares how two models with different structures and resolutions respond to a common set of population and climate drivers in the western U.S. The BEND model simulates hourly residential and commercial building electricity consumption at the county scale by weighting the results of simulations of thousands of representative buildings. In contrast, the projected electricity demand in GCAM-USA is determined for each state, year, sector (residential/commercial), and service (e.g., heating, cooling, and others), based on population, income, technology, energy prices, and average annual climate. This paper aggregates the two models’ results to a common resolution: annual residential and commercial building electricity consumption by state. Both models show similar responses to future projected population change and climate change, with population change having the larger impact between the two. Differences are primarily due to how the models capture changes in the aggregate energy efficiency of the building stock as it evolves over time.

Projection of residential and commercial electricity consumption under SSPs in Jiangsu province, China

Future electricity consumption may increase due to climate change, but the amplitude depends on the interaction between many uncertain mechanisms. Based on the linear model and policy model, the residential and commercial electricity consumption in Jiangsu province are projected under the shared socioeconomic pathways (SSPs). The linear model considers climate and socioeconomic factors, and the policy model also takes policy factors into account. We find that the cooling degree days (CDD) coefficient is about 3 times of heating degree days (HDD), which reflects that the cooling demand is much larger than heating, and also shows in the projection. The results of the policy model are generally lower than the linear model, which is the impact of policy factors. For example, the SSP1 and SSP2 of the policy model are 320 TW h and 241.6 TW h lower than the linear model in 2100, respectively. At the end of the 21st century, the residential and commercial electricity consumption in Jiangsu province will reach 107.7–937.9 TW h per year, 1.3–11.6 times of 2010. The SSP1 scenario under the policy model is based on feasible assumptions, and can be used as the target scenario for policymakers to establish energy intensity reduction targets.

Effective management of plug loads in commercial buildings with occupant engagement and centralized controls

Plug loads, or devices plugged into wall outlets, comprise a growing proportion of commercial building electricity consumption. This study presents the results of a hybrid approach for plug load management, which includes a combination of occupant engagement and centralized controls. Electricity consumption was monitored for 249 plug load devices in the XFINITY Center at the University of Maryland. After a baseline monitoring period, 1) on-off schedules were set for 78 devices in the concession stands, and 2) 31 office occupants in the facility took part in an energy competition and were given access to a mobile energy app (Keewi, Inc.) to control their 115 devices. Centralized controls resulted in a reduction of 58% in consumption in the concessions during the days when the concessions were not in use. Occupant engagement led to a reduction of 32% in plug load electricity consumption in the offices. Savings were largest in high-consumption categories such as merchandisers and food preparation chillers in the concessions, and IT equipment, refrigerators, desktop computers, and space heaters in the offices. The study indicates the benefits of hybrid approaches to plug load management given unique facility characteristics.

Forecasting the Electricity Demand and Market Shares in Retail Electricity Market Based on System Dynamics and Markov Chain

Due to the deregulation of retail electricity market, consumers can choose retail electric suppliers freely, and market entities are facing fierce competition because of the increasing number of new entrants. Under these circumstances, forecasting the changes in all market entities, when market share stabilized, is important for suppliers making marketing decisions. In this paper, a market share forecasting model was established based on Markov chain, and a system dynamics model was constructed to forecast the electricity consumption based on the analysis of five factors which are economic development, policy factors, environmental factors, power energy substitution, and power grid development. For a real application, the retail electricity market of Guangdong province in China was selected. The total, industrial, and commercial electricity consumption in Guangdong from 2016 to 2020 were predicted under different scenarios, and the market shares of the main market entities were analyzed using Markov chain model. Results indicated that the direct trading electricity would account for 70% to 90% of the total electricity consumption in the future. This provided valuable reference for the decision-making of suppliers and the development of electricity industry.

Accounting for Nonlinearity, Asymmetry, Heterogeneity, and Cross-Sectional Dependence in Energy Modeling: US State-Level Panel Analysis

This paper provides an example of several modeling and econometric advances used in the panel estimation of energy demand elasticities. The paper models the demand of total, industrial, and transport energy consumption and residential and commercial electricity consumption by analyzing US state-based panel data. The paper employs recently developed dynamic panel methods that address heterogeneity, nonstationarity, and cross-sectional dependence. In addition, the paper (i) considers possible nonlinear relationships between energy consumption and income without employing polynomial transformations of integrated income; and (ii) allows for and calculates possible asymmetric relationships between energy consumption and price. Finally, the paper models energy efficiency improvements by a nonlinear time trend. To our knowledge no other paper has combined all of the econometric and modeling advances that are applied here. Most of the results conformed to expectations; however, limited to no evidence of nonlinearities and asymmetries were uncovered.

Modeling of Monthly Residential and Commercial Electricity Consumption Using Nonlinear Seasonal Models—The Case of Hong Kong

Accurate modeling and forecasting monthly electricity consumption are the keys to optimizing energy management and planning. This paper examines the seasonal characteristics of electricity consumption in Hong Kong—a subtropical city with 7 million people. Using the data from January 1970 to December 2014, two novel nonlinear seasonal models for electricity consumption in the residential and commercial sectors were obtained. The models show that the city’s monthly residential and commercial electricity consumption patterns have different seasonal variations. Specifically, monthly residential electricity consumption (mainly for appliances and cooling in summer) has a quadratic relationship with monthly mean air temperature, while monthly commercial electricity consumption has a linear relationship with monthly mean air temperature. The nonlinear seasonal models were used to predict residential and commercial electricity consumption for the period January 2015–December 2016. The correlations between the predicted and actual values were 0.976 for residential electricity consumption and 0.962 for commercial electricity consumption, respectively. The root mean square percentage errors for the predicted monthly residential and commercial electricity consumption were 7.0% and 6.5%, respectively. The new nonlinear seasonal models can be applied to other subtropical urban areas, and recommendations on the reduction of commercial electricity consumption are given.