THE EFFECT OF OCCUPANCY ON ELECTRICITY USE IN THREE CANADIAN SCHOOLS

2018 ◽  
Vol 13 (1) ◽  
pp. 95-112
Author(s):  
Mohamed Ouf ◽  
Mohamed H. Issa ◽  
Phil Merkel ◽  
Panos Polyzois
Keyword(s):  
Energy Consumption ◽  
Real Time ◽  
Electricity Consumption ◽  
After School ◽  
Building Performance ◽  
Comprehensive Method ◽  
Electricity Use ◽  
Period 2 ◽  
Consumption Data ◽  
Plug Loads

Through building performance simulations, previous studies showed the effect of occupants on buildings' energy consumption. To further demonstrate this effect using empirical evidence, this study analyzed the effect of occupancy on real-time electricity consumption in three case-study schools in Manitoba. Within each school, one classroom as well as the gymnasium were sub-metered to collect real-time electricity consumption data at half-hourly intervals. The study focused on electricity consumption for lighting and plug loads, which make up 30% of energy consumption in Canadian commercial and institutional buildings. A comprehensive method was developed to investigate energy-related occupant behaviour in the sub-metered spaces using four different tools simultaneously: 1) gymnasium bookings after school hours over a four-month period, 2) half-hourly observations of lighting and equipment use in the sub-metered spaces in each school over a two-week period, 3) a daily survey administered to teachers in the sub-metered classrooms over a two-week period, and 4) occupancy and light sensors to evaluate actual recorded occupancy and light use durations over a four-month period. Results showed that recorded occupancy durations over a 4-month period only explained less than 10% of the variations in classrooms' lighting electricity consumption, meaning that lights may have been used frequently while classrooms were unoccupied. Results also showed the differences in gymnasiums' electricity consumption were still statistically significant between the three schools, even after school hours and when the gymnasiums were not used or booked for other activities. This study is the first to provide in-depth evaluation of the effect of occupancy on electricity consumption in Canadian schools.

Real-Time Highly Resolved Spatial-Temporal Vehicle Energy Consumption Estimation Using Machine Learning and Probe Data

2021 ◽  
pp. 036119812110391
Author(s):  
Joseph Severino ◽  
Yi Hou ◽  
Ambarish Nag ◽  
Jacob Holden ◽  
Lei Zhu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Real Time ◽  
Volume Estimation ◽  
Energy Estimates ◽  
Volume Data ◽  
Road Transportation ◽  
Energy Efficient Routing ◽  
Real World Data ◽  
Consumption Data

Real-time highly resolved spatial-temporal vehicle energy consumption is a key missing dimension in transportation data. Most roadway link-level vehicle energy consumption data are estimated using average annual daily traffic measures derived from the Highway Performance Monitoring System; however, this method does not reflect day-to-day energy consumption fluctuations. As transportation planners and operators are becoming more environmentally attentive, they need accurate real-time link-level vehicle energy consumption data to assess energy and emissions; to incentivize energy-efficient routing; and to estimate energy impact caused by congestion, major events, and severe weather. This paper presents a computational workflow to automate the estimation of time-resolved vehicle energy consumption for each link in a road network of interest using vehicle probe speed and count data in conjunction with machine learning methods in real time. The real-time pipeline can deliver energy estimates within a couple seconds on query to its interface. The proposed method was evaluated on the transportation network of the metropolitan area of Chattanooga, Tennessee. The volume estimation results were validated with ground truth traffic volume data collected in the field. To demonstrate the effectiveness of the proposed method, the energy consumption pipeline was applied to real-world data to quantify road transportation-related energy reduction because of mitigation policies to slow the spread of COVID-19 and to measure energy loss resulting from congestion.

Modelling of energy consumption in sociotechnical systems with intelligent equipment

2021 ◽  
pp. 4-19
Author(s):  
Владимир Борисович Барахнин ◽  
Светлана Валентиновна Мальцева ◽  
Константин Владимирович Данилов ◽  
Василий Вячеславович Корнилов
Keyword(s):  
Energy Consumption ◽  
Energy Use ◽  
Electricity Consumption ◽  
Feedback Mechanism ◽  
Smart Devices ◽  
Consumption Function ◽  
Management Processes ◽  
Consumption Data ◽  
The Times ◽  
Dynamic Time

Современные социотехнические системы в различных областях характеризуются наличием в их составе большого количества интеллектуального оборудования, которое может самостоятельно регулировать собственное потребление энергии, а также взаимодействовать с другими потребителями в процессах принятия решений и управления. Одна из таких отраслей - энергетика, где самоорганизация и системы коллективного потребления являются наиболее перспективными с точки зрения обеспечения эффективности использования энергоресурсов. Рассмотрены подходы к установлению статических и динамических тарифов на электроэнергию. Проведено сравнение двух моделей энергопотребления - статического двухтарифного и динамического, учитывающих рациональное поведение умных устройств, способных выбирать лучшие режимы для потребления электроэнергии. Показано влияние количества таких устройств на возможность достижения равномерного потребления при использовании второй модели. Modern socio-technical systems in various fields include a large number of smart equipment that can independently regulate its own energy consumption, as well as interact with other consumers in decision-making and management processes. Energy is one of these areas. Self-organization and collective self-consumption are the most promising in terms of ensuring the efficiency of energy use. Existing and prospective approaches to using static and dynamic time-based tariffs are under consideration. The paper presents a mathematical description of two models of energy consumption: a static model based on the allocation of two zones with a fixed duration and tariffs for each one and a dynamic model of two-tariff accounting with feedback, which assumes tariffs changing based on the results of the analysis of current electricity consumption. A pilot study of both models was conducted by using energy consumption data and taking into account the rational behavior of smart devices as consumers who can choose the best periods for electricity consumption. During the experiments it was investigated how an increase in the share of smart devices in the composition of electricity consumers as well as options for establishing zones and tariffs, affect the possibility of achieving uniform consumption during the day. Experiments have shown that with a small proportion of smart devices, acceptable results that reduce the variation in the consumption function can favor usage of the model without feedback. An increase in the number of actors in the system inevitably requires including a feedback mechanism into the system that allows the resource supplier to prevent excessive concentration of smart devices during the period of the cheaper tariff. However, when the share of smart devices exceeds a certain critical value, a pronounced inversion of the times of cheap and expensive tariffs occurs in two successive iterations. In this case, in order to ensure a quite even distribution of electricity consumption, it is advisable for the supplier to return to the single tariff rate. Thus, an excessive increase in the number of actors in the system can neutralize the effect of their use

Analysis and Design of Electric Energy Consumption Inspection Equipment Based on ARM9

2012 ◽  
Vol 157-158 ◽  
pp. 447-451
Author(s):  
Hu Hu ◽  
Xin Tian ◽  
Li Hong Han ◽  
Bin Chen
Keyword(s):  
Energy Consumption ◽  
Real Time ◽  
Low Cost ◽  
Electric Energy ◽  
Basic Structure ◽  
Analysis And Design ◽  
Electric Energy Consumption ◽  
Real Time Analysis ◽  
Consumption Data ◽  
Real Time Transmission

The present paper introduces a sort of analysis and design of electric energy consumption inspection equipment based on ARM9, which can inspect multiple electric energy indexes and conduct a real time inspection to electric energy consumption. Both a real time collection and a real time transmission of electric energy consumption data are realized and a real time analysis of these data that are transmitted through the network to the host computer can be carried out as well, the features of which are low power consumption, low cost, very applicable, high real time performance, etc. The paper also describes the system’s basic structure, hardware design, software design and system debugging process.

scholarly journals Bagging Ensemble of Multilayer Perceptrons for Missing Electricity Consumption Data Imputation

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1772 ◽  
Author(s):  
Seungwon Jung ◽  
Jihoon Moon ◽  
Sungwoo Park ◽  
Seungmin Rho ◽  
Sung Wook Baik ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Energy Management ◽  
Missing Values ◽  
Electric Energy ◽  
Electricity Consumption ◽  
Multilayer Perceptrons ◽  
Imputation Methods ◽  
Electric Energy Consumption ◽  
Consumption Data ◽  
Bagging Ensemble

For efficient and effective energy management, accurate energy consumption forecasting is required in energy management systems (EMSs). Recently, several artificial intelligence-based techniques have been proposed for accurate electric load forecasting; moreover, perfect energy consumption data are critical for the prediction. However, owing to diverse reasons, such as device malfunctions and signal transmission errors, missing data are frequently observed in the actual data. Previously, many imputation methods have been proposed to compensate for missing values; however, these methods have achieved limited success in imputing electric energy consumption data because the period of data missing is long and the dependency on historical data is high. In this study, we propose a novel missing-value imputation scheme for electricity consumption data. The proposed scheme uses a bagging ensemble of multilayer perceptrons (MLPs), called softmax ensemble network, wherein the ensemble weight of each MLP is determined by a softmax function. This ensemble network learns electric energy consumption data with explanatory variables and imputes missing values in this data. To evaluate the performance of our scheme, we performed diverse experiments on real electric energy consumption data and confirmed that the proposed scheme can deliver superior performance compared to other imputation methods.

scholarly journals What goes on behind closed doors?

2016 ◽  
Vol 17 (4) ◽  
pp. 451-470 ◽  
Author(s):  
Nicole Sintov ◽  
Ellen Dux ◽  
Agassi Tran ◽  
Michael Orosz
Keyword(s):  
Energy Consumption ◽  
Real Time ◽  
Carbon Dioxide Emissions ◽  
Group Identification ◽  
Environmental Behaviors ◽  
Content Type ◽  
Individual Level ◽  
Electricity Use ◽  
Reduce Energy Consumption ◽  
The Impact

Purpose The purpose of this paper was to evaluate the impact of a competition-based intervention combining high-resolution electricity feedback, incentives, information and prompts on college dormitory residents’ energy consumption and participation in demand response events. The authors also investigated changes in individual-level pro-environmental behaviors and examined psychosocial correlates of behavior change. Design/methodology/approach Residents of 39 suites in a freshman residence hall competed against one another to reduce energy consumption and win prizes as part of a three-week competition. Feedback was provided in near real-time at the suite-level via an interactive touch-screen kiosk. Participants also completed baseline and follow-up surveys. Findings Electricity use among all suites was approximately 6.4 per cent lower during the competition period compared to baseline, a significant reduction. Additionally, participants reported engaging in various pro-environmental behaviors significantly more frequently during the competition relative to baseline. Changes in pro-environmental behavior were associated with changes in level of group identification and perceived social norms. Practical implications In three weeks, dormitory residents saved 3,158 kWh of electricity compared to baseline – the equivalent of more than 3,470 pounds of carbon dioxide emissions. The findings provide evidence that real-time feedback, combined with incentives, information and prompts, can motivate on-campus residents to reduce energy consumption. Originality/value The authors contribute to a limited body of evidence supporting the effectiveness of dorm energy competitions in motivating college students to save energy. In addition, the authors identified individual-level behavioral and psychosocial changes made during such an intervention. University residential life planners may also use the results of this research to inform student programming.

scholarly journals Energy Consumption Prediction of Electric Vehicles Based on Digital Twin Technology

2021 ◽  
Vol 12 (4) ◽  
pp. 160
Author(s):  
Zhaolong Zhang ◽  
Yuan Zou ◽  
Teng Zhou ◽  
Xudong Zhang ◽  
Zhifeng Xu
Keyword(s):  
Energy Consumption ◽  
Real Time ◽  
Electric Vehicle ◽  
Digital Twin ◽  
Twin Model ◽  
Consumption Data ◽  
Energy Consumption Model ◽  
Energy Consumption Prediction ◽  
Consumption Prediction ◽  
Monitoring Platform

Digital twinning technology originated in the field of aerospace. The real-time and bidirectional feature of data interaction guarantees its advantages of high accuracy, real-time performance and scalability. In this paper the digital twin technology was introduced to electric vehicle energy consumption research. First, an energy consumption model of an electric vehicle of BAIC BJEV was established, then the model was optimized and verified through the energy consumption data of the drum test. Based on the data of the vehicle real-time monitoring platform, a digital twin model was built, and it was trained and updated by daily new data. Eventually it can be used to predict and verify the data of vehicle. In this way the prediction of energy consumption of vehicles can be achieved.

scholarly journals A Novel IoT Based Smart Energy Meter with Backup Battery

2021 ◽  
pp. 357-364
Author(s):  
Osman Yakubu ◽  
C. Narendra Babu ◽  
C. Osei Adjei
Keyword(s):  
Energy Consumption ◽  
Real Time ◽  
Energy Demand ◽  
Single Phase ◽  
Data Repository ◽  
Smart Meters ◽  
Cloud Data ◽  
Energy Meter ◽  
Consumption Data ◽  
Utility Companies

Energy consumption is currently on the ascendency due to increased demand by domestic and industrial consumers. The quest to ensure that consumers manage their consumption and the utility companies also monitor consumers to manage energy demand and production resulted in smart energy meters which are able to transmit data automatically at certain intervals being introduced. These Smart Meters are still fraught with challenges as consumers are unable to effectively monitor their consumption and the meters are also expensive to deploy. This research aims to present a novel IoT based Smart Energy Meter that will gather consumption data in real time and transmit it to a cloud data repository for storage and analysis. The novelty of this inexpensive system is the introduction of an ADM25SC Single Phase DIN-RAIL Watt-hour Energy Meter which sends power to the microcontroller and also the introduction of a backup battery that keeps the meter on for some time to transmit outage data during power outages. Data gathered from the proposed IoT based Smart Energy Meter for a period is compared against that of the same period from a Smart G meter, a widely used energy meter, and is found to be very close confirming the accuracy of the IoT based Smart Energy Meter.

scholarly journals Electricity Use as a Real-Time Indicator of the Economic Burden of the COVID-19-Related Lockdown: Evidence from Switzerland

2020 ◽  
Vol 66 (4) ◽  
pp. 303-321
Author(s):  
Benedikt Janzen ◽  
Doina Radulescu
Keyword(s):  
Real Time ◽  
Electricity Consumption ◽  
Economic Effects ◽  
Federal Regulations ◽  
Construction Sites ◽  
Jel Codes ◽  
Industrial Companies ◽  
Electricity Use ◽  
Electricity Load ◽  
Per Capita

Abstract We employ hourly electricity load data for Switzerland as a real-time indicator of the economic effects of the lockdown following the spread of SARS-CoV-2. Our findings reveal that following the drastic lockdown, overall electricity use decreased by 4.6%, with a reduction of even 14.3% in the Canton of Ticino where the number of confirmed cases per capita was one of the highest in Switzerland and also stricter measures such as closures of construction sites and industrial companies were implemented on top of federal regulations. Looking at working days only, we estimate a Swiss-wide decrease in electricity consumption of 7.4%. Assuming industry, services, transport, and agriculture account for 67% of electricity demand, the 4.6% decrease in electricity use implies an almost 7% output reduction in these sectors. In addition, the reduced electricity imports and the change in the generation mix of neighbouring countries, also translates into reduced CO2 emissions related to these imports. (JEL codes: C53, Q4, C3)

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