scholarly journals Automatic socio-economic classification of households using electricity consumption data

2013 ◽  
Author(s):  
Christian Beckel ◽  
Leyna Sadamori ◽  
Silvia Santini
Keyword(s):  
Electricity Consumption ◽  
Consumption Data

Private Information Inference of Households from Electricity Consumption Data

2021 ◽  
Author(s):  
Mert Pekey ◽  
Yigit Deniz Celebi ◽  
Ceren Anil ◽  
Albert Levi
Keyword(s):  
Private Information ◽  
Electricity Consumption ◽  
Consumption Data

Building electricity consumption: Data analytics of building operations with classical time series decomposition and case based subsetting

2018 ◽  
Vol 177 ◽  
pp. 184-196 ◽  
Author(s):  
Ethan M. Pickering ◽  
Mohammad A. Hossain ◽  
Roger H. French ◽  
Alexis R. Abramson
Keyword(s):  
Time Series ◽  
Data Analytics ◽  
Electricity Consumption ◽  
Time Series Decomposition ◽  
Consumption Data ◽  
Case Based

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

Forecasting Electricity Consumption Data from Paraguay Using a Machine Learning Approach

2021 ◽  
pp. 685-694
Author(s):  
José A. Gallardo ◽  
Miguel García-Torres ◽  
Francisco Gómez-Vela ◽  
Félix Morales ◽  
Federico Divina ◽  
...  
Keyword(s):  
Machine Learning ◽  
Electricity Consumption ◽  
Learning Approach ◽  
Consumption Data ◽  
Machine Learning Approach

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.

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