scholarly journals MFRED, 10 second interval real and reactive power for groups of 390 US apartments of varying size and vintage

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Christoph J. Meinrenken ◽  
Noah Rauschkolb ◽  
Sanjmeet Abrol ◽  
Tuhin Chakrabarty ◽  
Victor C. Decalf ◽  
...  
Keyword(s):  
Energy Use ◽  
Reactive Power ◽  
Cooling System ◽  
High Time Resolution ◽  
Annual Average ◽  
Electricity Use ◽  
Load Monitoring ◽  
Residential Electricity ◽  
Data Points ◽  
Integration Of Renewables

Abstract Building electricity is a major component of global energy use and its environmental impacts. Detailed data on residential electricity use have many interrelated research applications, from energy conservation to non-intrusive load monitoring, energy storage, integration of renewables, and electric vs. fossil-based heating. The dataset presented here, Multifamily Residential Electricity Dataset (MFRED), contains the electricity use of 390 apartments, ranging from studios to four-bedroom units. All apartments are located in the Northeastern United States (IECC-climate-zone 4 A), but differ in their heating/cooling system and construction year (early to late 20th century). To adhere to privacy guidelines, data were averaged across 15 apartments each, based on annual electricity use. MFRED includes real and reactive power, at 10-second resolution, for January to December 2019 (246 million data points). The annual average real power per apartment is 343 W (3.27 W/m2 of floor area), with strong variation between seasons and apartment size. Considering its large number of apartments, high time resolution, real and reactive power, and 12-month duration, MFRED is currently unique for the multifamily-sector.

scholarly journals A non-intrusive identification of home appliances using active power and harmonic current

2017 ◽  
Vol 30 (2) ◽  
pp. 199-208 ◽  
Author(s):  
Srdjan Djordjevic ◽  
Marko Dimitrijevic ◽  
Vanco Litovski
Keyword(s):  
Energy Use ◽  
Reactive Power ◽  
Poor Performance ◽  
Active Power ◽  
Harmonic Current ◽  
Monitoring Method ◽  
Harmonic Content ◽  
Active And Reactive Power ◽  
Load Monitoring ◽  
Electrical Consumption

In recent years, research on non-intrusive load monitoring has become very popular since it allows customers to better manage their energy use and reduce electrical consumption. The traditional non-intrusive load monitoring method, which uses active and reactive power as signatures, has poor performance in detecting small non-linear loads. This drawback has become more prominent because the use of nonlinear appliances has increased continuously during the last decades. To address this problem, we propose a NILM method that utilizes harmonic current in combination with the changes of real power. The advantages of the proposed method with respect to the existing frequency analysis based NILM methods are lower computational complexity and the use of only one feature to characterize the harmonic content of the current.

scholarly journals Drivers behind Residential Electricity Demand Fluctuations Due to COVID-19 Restrictions

Energies ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 5738
Author(s):  
Stephen Snow ◽  
Richard Bean ◽  
Mashhuda Glencross ◽  
Neil Horrocks
Keyword(s):  
Energy Demand ◽  
Energy Use ◽  
Air Conditioner ◽  
Electricity Use ◽  
Residential Electricity ◽  
Demand Fluctuations ◽  
Key Drivers ◽  
The Government ◽  
Device Use ◽  
Depth Interviews

The COVID-19 pandemic rapidly reoriented the lives of billions of people across the globe toward working, learning, and subsisting from home. This paper examines the consequences of this disruption of electricity use in Australian households. Using high-frequency electricity monitoring from 491 houses and per-circuit monitoring and in-depth interviews with 17 households, the paper (1) compares changes in energy use before and during COVID-19 lockdown, (2) quantifies the key drivers of changes in energy use experienced by households during lockdown, and (3) tracks households’ interactions with energy use feedback. The findings identify significant increases in certain aspects of household electricity use directly related to COVID-19, including increased cooking and digital device use. Yet despite the government mandate requiring a large proportion of the population to remain at home, overall energy use among the majority of Queensland households monitored actually decreased during lockdown versus prior, driven primarily by a reduction in air conditioner use during lockdown as the weather cooled. Further, despite significant quantified and self-reported changes in energy use, users who had energy use feedback installed accessed their dashboards less during lockdown than they did prior. The paper discusses these results in the context of statistics on COVID-19 related energy demand fluctuations elsewhere, and the implications for the provision of energy use information to residents during significant disruptions such as lockdown.

scholarly journals Price-Based Demand Side Response Programs and Their Effectiveness on the Example of TOU Electricity Tariff for Residential Consumers

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 287
Author(s):  
Jerzy Andruszkiewicz ◽  
Józef Lorenc ◽  
Agnieszka Weychan
Keyword(s):  
Power Systems ◽  
Distribution System ◽  
Peak Load ◽  
Demand Side ◽  
Case Study Analysis ◽  
Efficiency Assessment ◽  
Electricity Use ◽  
Residential Electricity ◽  
Demand Side Response ◽  
Residential Consumers

Demand side response is becoming an increasingly significant issue for reliable power systems’ operation. Therefore, it is desirable to ensure high effectiveness of such programs, including electricity tariffs. The purpose of the study is developing a method for analysing electricity tariff’s effectiveness in terms of demand side response purposes based on statistical data concerning tariffs’ use by the consumers and price elasticity of their electricity demand. A case-study analysis is presented for residential electricity consumers, shifting the settlement and consequently the profile of electricity use from a flat to a time-of-use tariff, based on the comparison of the considered tariff groups. Additionally, a correlation analysis is suggested to verify tariffs’ influence of the power system’s peak load based on residential electricity tariffs in Poland. The presented analysis proves that large residential consumers aggregated by tariff incentives may have a significant impact on the power system’s load and this impact changes substantially for particular hours of a day or season. Such efficiency assessment may be used by both energy suppliers to optimize their market purchases and by distribution system operators in order to ensure adequate generation during peak load periods.

scholarly journals UNDERSTANDING THE EMBEDDED CARBON CHALLENGES OF BUILDING SERVICE SYSTEMS

2021 ◽  
Vol 1 ◽  
pp. 3279-3288
Author(s):  
Maria Hein ◽  
Darren Anthony Jones ◽  
Claudia Margot Eckert
Keyword(s):  
Energy Use ◽  
Cooling System ◽  
Current System ◽  
Energy Performance ◽  
Service Systems ◽  
Carbon Footprints ◽  
Embodied Carbon ◽  
Study Results ◽  
Operational Energy

AbstractEnergy consumed in buildings is a main contributor to CO2 emissions, there is therefore a need to improve the energy performance of buildings, particularly commercial buildings whereby building service systems are often substantially over-designed due to the application of excess margins during the design process.The cooling system of an NHS Hospital was studied and modelled in order to identify if the system was overdesigned, and to quantify the oversizing impact on the system operational and embodied carbon footprints. Looking at the operational energy use and environmental performance of the current system as well as an alternative optimised system through appropriate modelling and calculation, the case study results indicate significant environmental impacts are caused by the oversizing of cooling system.The study also established that it is currently more difficult to obtain an estimate of the embodied carbon footprint of building service systems. It is therefore the responsibility of the machine builders to provide information and data relating to the embodied carbon of their products, which in the longer term, this is likely to become a standard industry requirement.

scholarly journals Feature Identification for Non-Intrusively Extracting Occupant Energy-Use Information in Office Buildings

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Hamed Nabizadeh Rafsanjani
Keyword(s):  
Capital Investment ◽  
Energy Use ◽  
Low Cost ◽  
Specific Energy Consumption ◽  
Cost Effective ◽  
Office Buildings ◽  
Specific Power ◽  
Feature Identification ◽  
Load Monitoring ◽  
Energy Consuming

Detailed energy-use information of office buildings’ occupants is necessary to implement proper simulation/intervention techniques. However, acquiring accurate occupant-specific energy consumption in office buildings at low cost is currently a challenging task since existing intrusive load monitoring (ILM) technologies require a large capital investment to provide high-resolution electricity usage data for individual occupants. On the other hand, non-intrusive load monitoring (NILM) approaches have been proven as more cost effective and flexible approaches to provide energy-use information of individual appliances. Therefore, extending the concept of NILM to individual occupants would be beneficial. This paper proposes two occupancy-related energy-consuming features, delay interval and magnitude of power changes and evaluates their significances for extracting occupant-specific power changes in a non-intrusive manner. The proposed features were examined through implementing a logistic regression model as a predictor on aggregate energy load data collected from an office building. Hypotheses tests also confirmed that both features are statistically significant to non-intrusively derive individual occupants’ energy-use information. As the main contribution of this study, these features could be utilized in developing sophisticated NILM-based approaches to monitor individual occupant energy-consuming behavior.  

scholarly journals Performance Comparison of Artificial Intelligence Techniques for Non-intrusive Electrical Load Monitoring

2018 ◽  
Vol 7 (2) ◽  
pp. 143-152
Author(s):  
Khairuddin Khalid ◽  
Azah Mohamed ◽  
Ramizi Mohamed ◽  
Hussain Shareef
Keyword(s):  
Artificial Intelligence ◽  
Reactive Power ◽  
Performance Comparison ◽  
Support Vector ◽  
Fluorescent Light ◽  
Time Interval ◽  
Smart Meter ◽  
Air Conditioner ◽  
Artificial Intelligence Techniques ◽  
Load Monitoring

The increased awareness in reducing energy consumption and encouraging response from the use of smart meters have triggered the idea of non-intrusive load monitoring (NILM). The purpose of NILM is to obtain useful information about the usage of electrical appliances usually measured at the main entrance of electricity to obtain aggregate power signal by using a smart meter. The load operating states based on the on/off loads can be detected by analysing the aggregate power signals. This paper presents a comparative study for evaluating the performance of artificial intelligence techniques in classifying the type and operating states of three load types that are usually available in commercial buildings, such as fluorescent light, air-conditioner and personal computer. In this NILM study, experiments were carried out to collect information of the load usage pattern by using a commercial smart meter. From the power parameters captured by the smart meter, effective signal analysis has been done using the time time (TT)-transform to achieve accurate load disaggregation. Load feature selection is also considered by using three power parameters which are real power, reactive power and the TT-transform parameters. These three parameters are used as inputs for training the artificial intelligence techniques in classifying the type and operating states of the loads. The load classification results showed that the proposed extreme learning machine (ELM) technique has successfully achieved high accuracy and fast learning compared with artificial neural network and support vector machine. Based on validation results, ELM achieved the highest load classification with 100% accuracy for data sampled at 1 minute time interval.

scholarly journals Enhancing Non-intrusive Occupant Load Monitoring through Occupancy Matrix

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Hamed Nabizadeh Rafsanjani
Keyword(s):  
Energy Use ◽  
Cost Effective ◽  
Behavior Intervention ◽  
Built Environments ◽  
Load Variations ◽  
Office Space ◽  
Cost Effective Approach ◽  
Load Monitoring ◽  
Energy Consuming ◽  
The Moment

It has been universally accepted that energy consumption in commercial buildings is highly related to occupant behaviors. Improving occupants’ energy-use behaviors is regarded as the most cost-effective approach to enhance overall energy saving in commercial built environments. However, effective behavior intervention pursuits rely on the availability of occupant-specific energy-use information, which is extremely expensive to capture with existing technologies. In this context, the author’s previous studies proposed the non-intrusive occupant load monitoring (NIOLM) approach that captures individual occupants’ energy-consuming information at their entry and departure events in an economically feasible manner. The NIOLM assigns energy-load variations (ev) of a building to individual occupants and relies on two variables: Time delay intervals and magnitudes of ev. This paper extends the existing NIOLM concept with the inclusion of a new variable, the occupancy matrix which manifests the information of present occupants at the moment of ev. An experiment has been conducted in an office space to validate the feasibility and accuracy of the proposed approach. Outcomes of this research could be a great help for studies on occupant energy-use behaviors intervention and simulation. 

scholarly journals Synthesising Residential Electricity Load Profiles at the City Level Using a Weighted Proportion (Wepro) Model

Energies ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3543
Author(s):  
Angreine Kewo ◽  
Pinrolinvic D. K. Manembu ◽  
Per Sieverts Nielsen
Keyword(s):  
Energy Consumption ◽  
Energy Use ◽  
Labour Force ◽  
Large Energy ◽  
Load Profile ◽  
Simplified Approach ◽  
Residential Electricity ◽  
Electricity Load ◽  
The City ◽  
Weighted Proportion

It is important to understand residential energy use as it is a large energy consumption sector and the potential for change is of great importance for global energy sustainability. A large energy-saving potential and emission reduction potential can be achieved, among others, by understanding energy consumption patterns in more detail. However, existing studies show that it requires many input parameters or disaggregated individual end-uses input data to generate the load profiles. Therefore, we have developed a simplified approach, called weighted proportion (Wepro) model, to synthesise the residential electricity load profile by proportionally matching the city’s main characteristics: Age group, labour force and gender structure with the representative households profiles provided in the load profile generator. The findings indicate that the synthetic load profiles can represent the local electricity consumption characteristics in the case city of Amsterdam based on time variation analyses. The approach is in particular advantageous to tackle the drawbacks of the existing studies and the standard load model used by the utilities. Furthermore, the model is found to be more efficient in the computational process of the residential sector’s load profiles, given the number of households in the city that is represented in the local profile.

Evaluating Supply Chain Energy Use in a Smart Grid Paradigm

2011 ◽  
Author(s):  
Amip J. Shah ◽  
Kiara Corrigan
Keyword(s):  
Supply Chain ◽  
Value Chain ◽  
Energy Use ◽  
Small Subset ◽  
Time Data ◽  
Cost Structures ◽  
Energy Footprint ◽  
Electricity Use ◽  
Geographic Diversity

A key paradigm shift resulting from the intersection of the information technology (IT) and utility sectors is the availability of real-time data regarding energy use across different industries. Historically, ascertaining the energy costs across the value chain of a given product or service was a laborious and expensive task, requiring many months of data collection; several proxies or approximations for cases where measured data might not be cost-effectively available; and even then, the resulting energy footprint could have significant uncertainty based on time-of-measurement, geographic diversity of manufacturing sites, etc. As dynamic energy pricing begins to take hold and environmental externalities begin to be priced into existing cost structures, the ability to optimize a given value chain for minimal energy use becomes increasingly attractive. In this paper, we discuss an approach for leveraging dynamically available data alongside historical n-tier supply chain models to avail the ability for such optimization. The approach is illustrated for the case study of a computer manufacturer, where we find that metering electricity use at a small subset of sites can allow for a reasonable estimate of the total energy use across the supply chain.

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