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For apartment complexes receiving medium-voltage electrical energies, the apartments can choose an electricity charging method between the single and general contracts in the Republic of Korea. In the single contract, a residential high-voltage rate is applied to the total electrical energy consumptions of households and common areas. On the other hand, in the general contract, different rate plans are applied to the electrical energy consumptions of households and their common areas, where a generic high-voltage rate plan is applied to the common consumption. Hence, depending on the amounts and composition of the consumptions, both contracts have their own strengths and weaknesses in terms of the total electricity charge. The management office of an apartment complex can select its preferred contract considering the amount and composition of the power consumptions on an annual basis. In this paper, we first formulate a model for the contracts and analyze their properties based on Monte-Carlo simulations. We then observe the contract properties through actual metering data from 30 apartment complexes in Korea. From the analysis of this paper, we can select appropriate contract for a given apartment complex and have guidelines for saving electricity charges. The greater the consumption of the electrical energy and the common area portion, the more advantageous the general contract is in terms of reducing electricity charges.

Detection and localization of illegal electricity usage are important issue for power distribution companies. In order to detect illegal electricity usage, network current-based methods using smart meters were mostly used in previous researches. The main disadvantages of those methods are that they are unable to detect the exact location of illegal electricity usage. In addition, all users must be disconnected from the power system to detect the exact location. In this research, an inspection robot proposed for detecting and localizing of illegal electricity usage. The inspection robot can define location of illegal electricity usage on the air transmission line without disconnecting the end user’s electric connection. In addition, this method can indicate fault location of transmission line. This paper presents a novel mobile sensing-based localization method for illegal electricity usage by using an inspection robot, and it is verified through simulation and experiment results.

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.

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.

\"Nudges\" are being widely promoted to encourage energy conservation. We show that the popular electricity conservation \"nudge\" of providing feedback to households on own and peers' home electricity usage in a home electricity report is two to four times more effective with political liberals than with conservatives. Political conservatives are more likely than liberals to opt out of receiving the home electricity report and to report disliking the report. Our results suggest that energy conservation nudges need to be targeted to be most effective.

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.

Apartments account for 64.6% of all housing units in the Republic of Korea, and most of them receive electricity under a contract, which includes a progressive rate plan. Recently, due to the electrification of energy used in homes and the growing adoption of electric vehicles, electricity consumption in apartment complexes has been gradually increasing. Given the characteristics of the progressive rate system, an increase in electricity usage results in a significant higher rise in electricity bills. Thus, an effective alternative is required to reduce electricity bills for each household. In this paper, the savings in electricity bills achieved by reducing household electricity usage are analyzed from both apartment complex and individual household perspectives, using metering data from 13,332 households. When households are sorted by the amount of savings in descending order, the resulting values are found to follow a negative exponential curve. This indicates that the benefits from reducing electricity usage in households with higher saving are significantly larger compared to other ones. We analyzed bill savings when electricity usage reductions were selectively applied to the top 10%, 20%, and 30% of households with the largest savings. From the results, it is found that the largest savings in electricity bills for households are achieved when usage reductions are applied to the top 10% of households. It is expected that this amount of savings would encourage these households to reduce their electricity consumption. Additionally, it is found that the savings for apartment complexes and the total savings for selected households are not the same, resulting in changes in the bills for households that do not reduce their usage. From the results, it was observed that when the usage reduction of selected households is small or the proportion of households reducing usage is low, the common area charges for non-reducing households tend to increase, leading to higher electricity bills. On the contrary, when the usage reduction of selected households is large or the proportion of households reducing usage is high, the common area charges for non-reducing households tend to decrease, resulting in lower electricity bills.

One of the keys of enhancing the quality of electric power supply resides in the accuracy of the consumption metering. Nowadays development of the sensors, devices and systems for electricity metering offers the basis for this service. Nevertheless, this achievement in many situations is altered such that appropriate measures must be adopted even if already significant costs have been registered. In this paper is proposed and discussed an optimal solution based on the identification and minimizing the measurement errors for increasing the electricity readings accuracy and lowering the electricity losses and related costs. In this regard, a mathematical model was developed and a particular algorithm for the mentioned problem is proposed and tested in the case of a power distribution company where an enhancement on average of the own technological consumption with 4% was recorded.

This study examines the relationship between mobile app usage and residential electricity consumption. We focus on how smart meter feedback influences energy-saving behaviors under a progressive tariff system in South Korea. The study uses a combination of three datasets—daily electricity consumption, mobile app access logs, and demographic survey data—gathered from 284 households. A panel vector autoregression (VAR) model and a difference-in-difference (DID) approach are used to analyze the dynamic relationship between app engagement and energy use. The results show that daily app access does not significantly affect electricity consumption, on average. However, under a progressive tariff system, households nearing a tariff stage threshold demonstrate a reduction in electricity use when engaging with the app. This effect is strongest among households with smaller living areas, smaller household size, and no children. This study is among the first to provide empirical evidence on the impact of smart metering mobile apps in a real-world setting. Our findings underscore the importance of tailored feedback strategies to maximize energy efficiency through smart meter technology.

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.