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Energy consumption analysis has often faced challenges such as limited model accuracy and inadequate consideration of the complex interactions between energy usage and meteorological data. This study is presented as a solution to these challenges through a detailed analysis of energy consumption across UBC Campus buildings using a variety of machine learning models, including Neural Networks, Decision Trees, Random Forests, Gradient Boosting, AdaBoost, Linear Regression, Ridge Regression, Lasso Regression, Support Vector Regression, and K-Neighbors. The primary objective is to uncover the complex relationships between energy usage and meteorological data, addressing gaps in understanding how these variables impact consumption patterns in different campus buildings by considering factors such as seasons, hours of the day, and weather conditions. Significant interdependencies among electricity usage, hot water power, gas, and steam volume are revealed, highlighting the need for integrated energy management strategies. Strong negative correlations between Vancouver's temperature and energy consumption metrics are identified, suggesting opportunities for energy savings through temperature-responsive strategies, especially during warmer periods. Among the regression models evaluated, deep neural networks are found to excel in capturing complex patterns and achieve high predictive accuracy. Valuable insights for improving energy efficiency and sustainability practices are offered, aiding informed decision-making for energy resource management in educational campuses and similar urban environments. Applying advanced machine learning techniques underscores the potential of data-driven energy optimization strategies. Future research could investigate causal relationships between energy consumption and external factors, assess the impact of specific operational interventions, and explore integrating renewable energy sources into the campus energy mix. UBC can advance sustainable energy management through these efforts and can serve as a model for other institutions that aim to reduce their environmental impact. [ABSTRACT FROM AUTHOR]

The global data center (DC) sector has expanded rapidly during the last decades, due to the rising demand for digital services. In the Nordic region, Sweden has emerged as a global hub, attracting leading technology companies like Amazon, Facebook, Microsoft, and Google. Server halls of DCs are energy intensive buildings, which puts pressure on local water resources and contributes to global greenhouse gas emissions. This study aims to, firstly, quantify the environmental impact of DCs, based on energy usage, water consumption, and greenhouse gas (GHG) emissions. Secondly, it develops a planning tool by employing a multi-criteria approach to optimally locate new DCs and to assess the site suitability of existing ones in Sweden. Data of various performance indicators (geographical data on renewable energy accessibility, free cooling conditions, excess heat receivers, and resilience to water shortages) of DCs was collected through different means, e.g., questionnaire surveys, permit applications, company websites, and other open online data repositories. ArcGIS Pro was employed for spatial analysis, and 68 DCs with a site suitability index (SSI) ≤ 45 % were identified as less ideally located. The principal findings are centered on Sweden, and thereby primarily benefit stakeholders engaged in decision-making for evaluating existing or strategic planning of new DCs by incorporating a comprehensive environmental perspective. Given the rapidly changing climate, strategically siting DCs will become crucial for minimizing the sector's environmental impact.

The electricity usage in residential buildings in China has increased sharply in recent years, placing great pressure on the power supply. We use electricity usage data at the household level to analyze resident electricity usage behavior. Based on cluster analysis, we categorize electricity usage in residential buildings into three types: the summer-sensitivity type, winter-sensitivity type, and balanced type. Habits exert a significant effect on resident electricity usage and show different effects across different resident electricity usage patterns. We find that the electricity-saving potential of incorporating certain habits in summer and winter is 9.34% and 8.35%, respectively. Our findings have practical significance for reducing electricity usage in residential buildings. [ABSTRACT FROM AUTHOR]

Carbon reduction programs are being introduced for carbon neutrality and energy transition to clean energy sources in various sectors, such as energy, buildings, transportation, and agriculture. In the residential electricity energy of the energy sector, the time-of-use (TOU) rate plan, which employs dynamic rates depending on energy usage times based on the advanced metering infrastructure (AMI), is being implemented for efficient electricity energy consumption. For broad expansion of the TOU rate plan, customers need information about its benefits, such as potential savings on electricity bills. In this paper, we first analyze the statistical characteristics of electricity energy usage using the metering data collected from 10 apartment complexes through AMI and develop a model to calculate the electricity bill savings. We next introduce examples of major home appliances, of which usage times can be shifted, and offer projected bill savings from the developed model. We analyze the examples from both the perspectives of households and apartment complexes. The information from this analysis is helpful in practically investigating customers' willingness to shift the usage time for a successful implementation of the demand response program. [ABSTRACT FROM AUTHOR]

This study demonstrates a comparison of energy usage, land footprint, and volumetric requirements of municipal wastewater treatment with aerobic granular sludge (AGS) and conventional activated sludge (CAS) at a full‐scale wastewater treatment plant characterized by large fluctuations in nutrient loadings and temperature. The concentration of organic matter in the influent to the AGS was increased by means of hydrolysis and bypassing the pre‐settler. Both treatment lines produced effluent concentrations below 5 mg BOD7 L−1, 10 mg TN L−1, and 1 mg TP L−1, by enhanced biological nitrogen‐ and phosphorus removal. In this case study, the averages of volumetric energy usage over 1 year were 0.22 ± 0.08 and 0.26 ± 0.07 kWh m−3 for the AGS and CAS, respectively. A larger difference was observed for the energy usage per reduced population equivalents (P.E.), which was on average 0.19 ± 0.08 kWh P.E.−1 for the AGS and 0.30 ± 0.08 kWh P.E.−1 for the CAS. However, both processes had the potential for decreased energy usage. Over 1 year, both processes showed similar fluctuations in energy usage, related to variations in loading, temperature, and DO. The AGS had a lower specific area, 0.3 m2 m−3 d−1, compared to 0.6 m2 m−3 d−1 of the CAS, and also a lower specific volume, 1.3 m3 m−3 d−1 compared to 2.0 m3 m−3 d−1. This study confirms that AGS at full‐scale can be compact and still have comparable energy usage as CAS. Practitioner Points: Full‐scale case study comparison of aerobic granular sludge (AGS) and conventional activated sludge (CAS), operated in parallel.AGS had 50 % lower footprint compared to CAS.Energy usage was lower in the AGS, but both processes had potential to improve the energy usage efficiency.Both processes showed low average effluent concentrations. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic created large disruptions in the energy industry as governments all around the world responded by enforcing lockdowns on 54% of the global population to slow down the spread of the virus. This impacts the energy consumption of the residential sector as policies prevented residents from leaving their homes and accelerated the transition towards remote work environments. The impact of lockdown on South Africa's energy sector is unique to other countries due to its extreme inequalities across the affluence divide and ongoing blackouts that affect the entire country. A case study is performed in George, a region in South Africa with a distribution of wealth and energy inequality typical for South Africa. In this paper, the following questions are answered: (i) How has the lockdown period influenced the total energy consumption of a household? and (ii) To what extent has the lockdown affected energy consumption of households based on wealth and demographics? The results of this study showed that implementing the highest stage of lockdown resulted in wealthy communities increasing their overall energy consumption by 5 percentage points and poor communities by 2.5 percentage points. The findings of this study support the use of local data and insight for the unique difference in behavioural change across different communities for optimal decision making in future pandemics or natural disasters. [ABSTRACT FROM AUTHOR]

Carbon reduction programs are being introduced for carbon neutrality and energy transition to clean energy sources in various sectors, such as energy, buildings, transportation, and agriculture. In the residential electricity energy of the energy sector, the time-of-use (TOU) rate plan, which employs dynamic rates depending on energy usage times based on the advanced metering infrastructure (AMI), is being implemented for efficient electricity energy consumption. For broad expansion of the TOU rate plan, customers need information about its benefits, such as potential savings on electricity bills. In this paper, we first analyze the statistical characteristics of electricity energy usage using the metering data collected from 10 apartment complexes through AMI and develop a model to calculate the electricity bill savings. We next introduce examples of major home appliances, of which usage times can be shifted, and offer projected bill savings from the developed model. We analyze the examples from both the perspectives of households and apartment complexes. The information from this analysis is helpful in practically investigating customers’ willingness to shift the usage time for a successful implementation of the demand response program.

The COVID-19 pandemic created large disruptions in the energy industry as governments all around the world responded by enforcing lockdowns on 54% of the global population to slow down the spread of the virus. This impacts the energy consumption of the residential sector as policies prevented residents from leaving their homes and accelerated the transition towards remote work environments. The impact of lockdown on South Africa’s energy sector is unique to other countries due to its extreme inequalities across the affluence divide and ongoing blackouts that affect the entire country. A case study is performed in George, a region in South Africa with a distribution of wealth and energy inequality typical for South Africa. In this paper, the following questions are answered: (i) How has the lockdown period influenced the total energy consumption of a household? and (ii) To what extent has the lockdown affected energy consumption of households based on wealth and demographics? The results of this study showed that implementing the highest stage of lockdown resulted in wealthy communities increasing their overall energy consumption by 5 percentage points and poor communities by 2.5 percentage points. The findings of this study support the use of local data and insight for the unique difference in behavioural change across different communities for optimal decision making in future pandemics or natural disasters.

The global data center (DC) sector has expanded rapidly during the last decades, due to the rising demand for digital services. In the Nordic region, Sweden has emerged as a global hub, attracting leading technology companies like Amazon, Facebook, Microsoft, and Google. Server halls of DCs are energy intensive buildings, which puts pressure on local water resources and contributes to global greenhouse gas emissions. This study aims to, firstly, quantify the environmental impact of DCs, based on energy usage, water consumption, and greenhouse gas (GHG) emissions. Secondly, it develops a planning tool by employing a multi-criteria approach to optimally locate new DCs and to assess the site suitability of existing ones in Sweden. Data of various performance indicators (geographical data on renewable energy accessibility, free cooling conditions, excess heat receivers, and resilience to water shortages) of DCs was collected through different means, e.g., questionnaire surveys, permit applications, company websites, and other open online data repositories. ArcGIS Pro was employed for spatial analysis, and 68 DCs with a site suitability index (SSI) ≤ 45 % were identified as less ideally located. The principal findings are centered on Sweden, and thereby primarily benefit stakeholders engaged in decision-making for evaluating existing or strategic planning of new DCs by incorporating a comprehensive environmental perspective. Given the rapidly changing climate, strategically siting DCs will become crucial for minimizing the sector's environmental impact., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Poor cost recovery rates and large subsidization of the agriculture and households by industrial and commercial users are two of the characteristics of the power sector in India. The paper applies a variety of linear and nonlinear unit root tests and a Kalman filter approach which can host time varying elasticities, to estimate the effects of various macroeconomic factors on the price elasticity for electricity in India during 1975–2013. The estimated price elasticity is −0.21 and the income elasticity is 0.41. The study finds that the electricity demand is affected by macroeconomic parameters such as trade and foreign direct investment. The findings of the study have implications to the policy makers and private investors who wish to invest in electricity supply utilities and infrastructure. The value of the estimated elasticity in the study is relatively smaller as compared to the previous studies reflecting, among other things, the fact that electricity demand is supply driven in India. • The Kalman filter approach hosts time varying elasticities. • The estimated price elasticity is -0.21. • The estimated income elasticity is 0.41. • Electricity demand is affected by trade and foreign direct investment. • Electricity demand is supply driven in India. [ABSTRACT FROM AUTHOR]

This work presents an estimation of the global electricity usage that can be ascribed to Communication Technology (CT) between 2010 and 2030. The scope is three scenarios for use and production of consumer devices, communication networks and data centers. Three different scenarios, best, expected, and worst, are set up, which include annual numbers of sold devices, data traffic and electricity intensities/efficiencies. The most significant trend, regardless of scenario, is that the proportion of use-stage electricity by consumer devices will decrease and will be transferred to the networks and data centers. Still, it seems like wireless access networks will not be the main driver for electricity use. The analysis shows that for the worst-case scenario, CT could use as much as 51% of global electricity in 2030. This will happen if not enough improvement in electricity efficiency of wireless access networks and fixed access networks/data centers is possible. However, until 2030, globally-generated renewable electricity is likely to exceed the electricity demand of all networks and data centers. Nevertheless, the present investigation suggests, for the worst-case scenario, that CT electricity usage could contribute up to 23% of the globally released greenhouse gas emissions in 2030.

In order to mitigate the power density shortage of current energy storage systems (ESSs) in pure electric vehicles (PEVs or EVs), a hybrid ESS (HESS), which consists of a battery and a supercapacitor, is considered in this research. Due to the use of the two ESSs, an energy management should be carried out for the HESS. An optimal energy management strategy is proposed based on the Pontryagin's minimum principle in this research, which instantaneously distributes the required propulsion power to the two ESSs during the vehicle's propulsion and also instantaneously allocates the regenerative braking energy to the two ESSs during the vehicle's braking. The objective of the proposed energy management strategy is to minimize the electricity usage of the EV and meanwhile to maximize the battery lifetime. A simulation study is conducted for the proposed energy management strategy and also for a rule-based energy management strategy. The simulation results show that the proposed strategy saves electricity compared to the rule-based strategy and the single ESS case for the three typical driving cycles studied in this research. Meantime, the proposed strategy has the effect of prolonging the battery lifetime compared to the other two cases. [ABSTRACT FROM AUTHOR]

To enable small communities to benchmark the energy efficiency of their water resource recovery facilities (also known as wastewater treatment facilities), multiple linear regression models of electric and overall energy intensity (kWh/m3) were created using data from Nebraska and Pennsylvania. Key variables found to be significant include: facility type, supplemental energy usage for sludge treatment, average flow, percent design flow, climate controlled floor area, effluent NH3- N, and influent CBOD5. The results show that energy use models for small systems differ from those for large facilities and that regulatory changes can affect energy usage. Step changes in the data for facilities that changed operators highlight the importance of operational decisions on energy efficiency for small facilities serving fewer than 10,000 people. Differences were observed between the models of data from specific states. Although these models may not include all factors that account for variability in energy use, they can provide a reference benchmark for small WRRFs. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Management & Business Research (2521-4306) is the property of Chinese Management Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Investigation of household electricity usage patterns, and mat- ching the patterns to behaviours, is an important area of research given the centrality of such patterns in addressing the needs of the electricity indu- stry. Additional knowledge of household behaviours will allow more effective targeting of demand side management (DSM) techniques. This paper addresses the question as to whether a reasonable number of meaningful motifs, that each represent a regular activity within a domestic household, can be identified solely using the household level electricity meter data. Using UK data collected from several hundred households in Spring 2011 monitored at a frequency of five minutes, a process for finding repeating short patterns (motifs) is defined. Different ways of representing the motifs exist and a qualitative approach is presented that allows for choosing between the options based on the number of regular behaviours detected (neither too few nor too many). [ABSTRACT FROM AUTHOR]

Governments are increasingly adopting behavioral science techniques for changing individual behavior in pursuit of policy objectives. The types of "nudge" interventions that governments are now adopting alter people's decisions without coercion or significant changes to economic incentives. We calculated ratios of impact to cost for nudge interventions and for traditional policy tools, such as tax incentives and other financial inducements, and we found that nudge interventions often compare favorably with traditional interventions. We conclude that nudging is a valuable approach that should be used more often in conjunction with traditional policies, but more calculations are needed to determine the relative effectiveness of nudging. [ABSTRACT FROM AUTHOR]

Natural and human-caused extreme events can alter residential electricity demand in urban areas and stress the electricity grid, with different types of residential electricity consumers exhibiting different consumption patterns. Residential electricity demands have been widely analyzed considering single-family consumers; however, multi-family consumption patterns remain comparatively understudied. The deployment of smart electricity meters enables the identification of single- and multi-family residential electricity consumption patterns at high temporal resolution. Using smart electricity meter data for the greater Chicago area, we compare electricity demand profiles reported by smart meters from single- and multi-family consumers in a large and diverse urban environment to understand residential electricity patterns better. Our study comprehensively analyzes the daily electricity demand profiles of these two types of residential consumers to identify peak electricity consumption times and magnitudes. Results show that the electricity demand of both residential end-users follows similar time of use patterns, and single-family users approximately double the demand of multi-family users on a per household basis. We also present predictive models of the electricity demand with socioeconomic data at the zip code level. Predictive model results show that multiple linear regression models explain up to 62% and 41% of the mean daily electricity (MDE) demand of single- and multi-family users, respectively. The median age of occupants, percent age 65 and older, mean commute time, and percent high school or higher education are statistically significant predictors of the MDE demand of single-family users, with percent high school or higher education having the highest relative importance. Similarly, median building age, percent multi-family, percent female, median age of occupants, and mean commute time are statistically significant predictors of multi-family electricity consumption, with median age of occupants having the highest relative importance. Modeling electricity demand to uncover differences between single- and multi-family residential electricity demands can assist city planners and utility managers to develop tailored demand management strategies. • Description of electricity demand from single- and multi-family residential users. • Regression model using electricity demand and socioeconomic data from the ACS. • Model explains 62% of the single-family demand and 41% of the multi-family demand. • For single-family accounts, the most important predictor is "Percent of occupancy". • For multi-family, the most important predictor is "Median age of occupants". [ABSTRACT FROM AUTHOR]

The evolution of electricity grids into a smart grid requires the inclusion of control systems to control load, flatten peaks and ensure the distribution of electricity. In parallel, the building sector will also be incorporating more control technology and put emphasis on sustainability issues such as reducing CO 2 emissions associated with the buildings’ electricity consumption. This article aims at modelling the residential sector and assesses the different levels of technology deployment to control the electricity consumption of household appliances. The number of inhabitants and their habits are also considered, and the response levels of users towards control systems are simulated. For this matter, a Markov-chain algorithm was developed for synthesising the electric load and introducing Home Energy Management System (HEMS). The emission levels from electricity consumption were assessed based on hourly CO 2 emission data from electricity production in Finland. Numerous electricity pricing models were also included, to assess the economic impacts of HEMS. The article suggests that a fully deployed HEMS may not be profitable for households with a low number of inhabitants. This is because the power consumption of appliances in stand-by mode offsets the positive impacts of HEMS on the electricity consumption profile. [ABSTRACT FROM AUTHOR]

We empirically examine the asset pricing implications of the Beckerian framework of household production, where utility is derived from both market consumption and home produced goods. We propose residential electricity usage as a real-time proxy for the service flow from household capital, because electricity is used in most modern-day household production activities and it cannot be easily stored. Using U.S. residential electricity usage from 1955 to 2012, our model based on household production explains the equity premium and the cross section of expected stock returns (including those of industry portfolios) with an R² of 71%. [ABSTRACT FROM AUTHOR]

We use a unique dataset that combines an original survey of households, information about the structural characteristics of their homes, utility-provided electricity usage records and program participation status, to study the uptake of energy efficiency incentives and their effect on residential electricity consumption. Attention is restricted to homes where heating and cooling is provided exclusively by air-source heat pumps. We deploy a difference-in-difference study design and find that replacing a heat pump with a new one does reduce electricity usage by 8% on average. The effect differs dramatically across households based upon whether they receive an incentive towards the purchase of a new heat pump. Among incentive recipients, the effect is small, and the larger the incentive, the smaller the reduction in electricity usage. These findings suggest that capital costs are incorporated into the (long-term) cost of energy, generating an apparent rebound effect that is much more pronounced for incentive recipients. [ABSTRACT FROM AUTHOR]

This work presents an estimation of the global electricity usage that can be ascribed to Communication Technology (CT) between 2010 and 2030. The scope is three scenarios for use and production of consumer devices, communication networks and data centers. Three different scenarios, best, expected, and worst, are set up, which include annual numbers of sold devices, data traffic and electricity intensities/efficiencies. The most significant trend, regardless of scenario, is that the proportion of use-stage electricity by consumer devices will decrease and will be transferred to the networks and data centers. Still, it seems like wireless access networks will not be the main driver for electricity use. The analysis shows that for the worst-case scenario, CT could use as much as 51% of global electricity in 2030. This will happen if not enough improvement in electricity efficiency of wireless access networks and fixed access networks/data centers is possible. However, until 2030, globally-generated renewable electricity is likely to exceed the electricity demand of all networks and data centers. Nevertheless, the present investigation suggests, for the worstcase scenario, that CT electricity usage could contribute up to 23% of the globally released greenhouse gas emissions in 2030. [ABSTRACT FROM AUTHOR]

Nearly a quarter of the world’s population has no access to electricity and nearly half cook with biomass or coal. Over the past 30 years there have been several initiatives to design and introduce appropriate, renewable energy technologies for household energy supply. However, few of these have been successful in terms of significant penetration and long-term sustainability. A new initiative, the SCORE project, was launched in 2007 with the objective of producing a clean biomass cook stove which also incorporates a thermo-acoustic engine which converts some of the heat into electricity. It was vital to determine the technical requirements of this new device as a pre-requisite to engineering development particularly in the two candidate countries where this new technology was to be trialled: Nepal and Uganda. Accurate data could not be sourced in the open literature and therefore studies were commissioned in order to obtain the relevant data. The data obtained are of significant value for any development projects involving the provision of small-scale off-grid electrical power and heat for cooking. A major purpose of the paper is hence to quantify current cooking and lighting habits in order to clarify some of the socio-economic issues that are important to the technical design of cooking and lighting systems. [ABSTRACT FROM PUBLISHER]

The article presents a study on the impact of tree shades on the energy consumption of residential houses in Auburn, Alabama. It states that the researchers used hedonic model as the analytical framework in 160 residencies in Auburn based on monthly electricity consumption. It concludes that tree shade reduces the electricity usage of an average house depending on the percentage of shade coverage of a house compared to those with no shades.

While past research has shown that providing residents with feedback about their electricity usage can reduce demand and its associated environmental burdens, some questions remain regarding what makes such feedback most effective. We followed the electricity usage of 36 residents who each received 14 feedback messages over 2 months. Using approaches borrowed from Natural-Language-Processing, feedbacks were generated automatically, using 10 features in random combinations. Unlike in previous studies, each resident received varying types of messages over time. In 504 observations, the average prompted reduction in electricity usage was 11 ± 3%, compared to a control group of 89 residents who received no messages. Feedback types prompting the largest reductions were self-comparisons with one's own earlier usage (average reduction 14%) and messages of high variety from one feedback-cycle to the next (average reduction 16%). Comparisons with neighbors did not prompt higher reductions on average. Instead, they prompted reductions only when a resident's recent usage happened to be higher than the average usage of neighbors, and increases when the reverse was true. This behavior was exhibited by all residents and is likely explained by a norm-conforming mean reversion of residents to their neighbors' average usage, rather than an anti-conform "boomerang" behavior previously suggested in similar contexts. [ABSTRACT FROM AUTHOR]

In response to the COVID-19 outbreak, the UK Government provided public health advice to stay at home from 16 March 2020, followed by instruction to stay at home (full lockdown) from 24 March 2020. We use data with high temporal resolution from utility sensors installed in 280 homes across social housing in Cornwall, UK, to test for changes in domestic electricity, gas and water usage in response to government guidance. Gas usage increased by 20% following advice to stay at home, the week before full lockdown, although no difference was seen during full lockdown itself. During full lockdown, morning electricity usage shifted to later in the day, decreasing at 6 a.m. and increasing at midday. These changes in energy were echoed in water usage, with a 17% increase and a one-hour delay in peak morning usage. Changes were consistent with people getting up later, spending more time at home and washing more during full lockdown. Evidence for these changes was also observed in later lockdowns, but not between lockdowns. Our findings suggest more compliance with an enforced stay-at-home message than with advice. We discuss implications for socioeconomically disadvantaged households given the indication of inability to achieve increased energy needs during the pandemic.