Modelling of energy consumption in sociotechnical systems with intelligent equipment

Author(s):  
Владимир Борисович Барахнин ◽  
Светлана Валентиновна Мальцева ◽  
Константин Владимирович Данилов ◽  
Василий Вячеславович Корнилов

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

Energies ◽  
2019 ◽  
Vol 12 (21) ◽  
pp. 4046 ◽  
Author(s):  
Sooyoun Cho ◽  
Jeehang Lee ◽  
Jumi Baek ◽  
Gi-Seok Kim ◽  
Seung-Bok Leigh

Although the latest energy-efficient buildings use a large number of sensors and measuring instruments to predict consumption more accurately, it is generally not possible to identify which data are the most valuable or key for analysis among the tens of thousands of data points. This study selected the electric energy as a subset of total building energy consumption because it accounts for more than 65% of the total building energy consumption, and identified the variables that contribute to electric energy use. However, this study aimed to confirm data from a building using clustering in machine learning, instead of a calculation method from engineering simulation, to examine the variables that were identified and determine whether these variables had a strong correlation with energy consumption. Three different methods confirmed that the major variables related to electric energy consumption were significant. This research has significance because it was able to identify the factors in electric energy, accounting for more than half of the total building energy consumption, that had a major effect on energy consumption and revealed that these key variables alone, not the default values of many different items in simulation analysis, can ensure the reliable prediction of energy consumption.


Author(s):  
Lindsey Kahn ◽  
Hamidreza Najafi

Abstract Lockdown measures and mobility restrictions to combat the spread of COVID-19 have impacted energy consumption patterns. The overall decline of energy use during lockdown restrictions can best be identified through the analysis of energy consumption by source and end-use sectors. Using monthly energy consumption data, the total 9-months use between January and September for the years 2015–2020 is calculated for each end-use sector (transportation, industrial, residential, and commercial). The cumulative consumption within these 9 months of the petroleum, natural gas, biomass, and electricity energy by the various end-use sectors are compared. The analysis shows that the transportation sector experienced the greatest decline (14.38%). To further analyze the impact of COVID-19 on each state within the USA, the consumption of electricity by each state and each end-use sector in the times before and during the pandemic is used to identify the impact of specific lockdown procedures on energy use. The distinction of state-by-state analysis in this study provides a unique metric for consumption forecasting. The average total consumption for each state was found for the years 2015–2019. The total average annual growth rate (AAGR) for 2020 was used to find a correlation coefficient between COVID-19 case and death rate, population density, and lockdown duration. A correlation coefficient was also calculated between the 2020 AAGR for all sectors and AAGR for each individual end-user. The results show that Indiana had the highest percent reduction in consumption of 10.07% while North Dakota had the highest consumption increase of 7.61%. This is likely due to the amount of industrial consumption relative to other sectors in the state.


2018 ◽  
Vol 13 (1) ◽  
pp. 95-112
Author(s):  
Mohamed Ouf ◽  
Mohamed H. Issa ◽  
Phil Merkel ◽  
Panos Polyzois

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.


2018 ◽  
Vol 61 (6) ◽  
pp. 1795-1810
Author(s):  
James Bambara ◽  
Andreas K. Athienitis

Abstract. The energy consumption of a building is significantly impacted by its envelope design, particularly for greenhouses where coverings typically provide high heat and daylight transmission. Energy and life cycle cost (LCC) analysis were used to identify the most cost-effective cladding design for a greenhouse located in Ottawa, Ontario, Canada (45.4° N) that employs supplemental lighting. The base case envelope design uses single glazing, whereas the two alternative designs consist of replacing the glass with twin-wall polycarbonate and adding foil-faced rigid insulation (permanent or movable) on the interior surface of the glass. All the alternative envelope designs increased electricity consumption for lighting and decreased heating energy use except when permanent or movable insulation was applied to the north wall and in the case of permanent insulation on the north wall plus polycarbonate on the east wall. This demonstrates how the use of reflective opaque insulation on the north wall can be beneficial for redirecting light onto the crops to achieve simultaneous reductions in electricity and heating energy costs. A maximum reduction in LCC of 5.5% (net savings of approximately $130,000) was achieved when permanent insulation was applied to the north and east walls plus polycarbonate on the west wall. This alternative envelope design increased electricity consumption for horticultural lighting by 4.3%, reduced heating energy use by 15.6%, and caused greenhouse gas emissions related to energy consumption to decrease by 14.7%. This analysis demonstrates how energy and economic analysis can be employed to determine the most suitable envelope design based on local climate and economic conditions. Keywords: Artificial lighting, Consistent daily light integral, Energy modeling, Envelope design, Greenhouse, Life cycle cost analysis, Light emitting diode, Local agriculture.


2016 ◽  
Vol 43 (2) ◽  
pp. 140-147 ◽  
Author(s):  
XIAODONG CHEN ◽  
JENNIFER DE LA ROSA ◽  
M. NILS PETERSON ◽  
YING ZHONG ◽  
CHUNTIAN LU

SUMMARYHousehold consumption is a major contributor to global greenhouse gas emissions. Some behaviours (for example energy use and vehicle use) may have far larger impacts than others (for example green consumerism of household products). Here, the driving forces of green consumerism and two domestic energy uses (electricity consumption and vehicle fuel use) are compared. This study found that environmental attitudes predicted green consumerism, but not electricity consumption or vehicle fuel use. Furthermore, green consumerism was correlated with income and individual level demographic factors, while energy consumption was primarily predicted by household size and structural constraints. Because household energy consumption has greater environmental impacts than green consumerism, policies that aim to improve pro-environmental attitudes may not be effective in mitigating greenhouse gas emissions. Policies should rather aim to change structural constraints influencing transportation and household energy decisions and improve the conspicuousness of household energy consumption.


Proceedings ◽  
2019 ◽  
Vol 31 (1) ◽  
pp. 32
Author(s):  
Jesús Fontecha ◽  
Iván González ◽  
Alberto Salas-Seguín

Today, households worldwide are being increasingly connected. Mobile devices and embedded systems carry out many tasks supported by applications which are based on artificial intelligence algorithms with the aim of leading homes to be smarter. One of the purposes of these systems is to connect appliances to the power network, as well as to the internet to monitor consumption data among others. In addition, new interaction ways are emerging to manage all these systems. For example, conversational assistants which allow us to interact by voice with devices at home. In this work, we present GreenMoCA, a system to monitor energy consumption data from connected devices at home with the aim of improving sustainability aspects and reducing such energy consumption, supported by a conversational assistant. This system is able to interact with the user in a natural way, providing information of current energy use and feedback based on previous consumption measures in a Smart Home environment. Finally, we assessed GreenMoCA from a usability and user experience approach on a group of users with positive results.


2020 ◽  
Vol 47 (3) ◽  
pp. 295-318
Author(s):  
Ebenezer Adesoji Olubiyi

The link among energy use, human welfare, and carbon emission has been a topical issue in the literature. In Africa, energy consumption has been on the increase owing to the production and consumption of sophisticated consumer goods and home appliances. Increased energy use triggers carbon emission that is detrimental to human welfare. This study investigates this puzzle in emerging African countries by utilizing panel vector autoregressive and system generalized method of moments (SYS-GMM) in the context of a mix of theories. The results indicate a unidirectional causality running from FUEL, COAL to per capita income (PCI). A unidirectional causality running from mortality rate (MOR) to COAL and CO2 was observed. There is a bidirectional relationship between MOR and energy use. The SYS-GMM results show that the effects of energy consumption on well-being are diverse. Increase in coal consumption reduces unemployment rate while electricity consumption reduces infant mortality rate. Fuel consumption aggravates incidence of mortality rate. CO2 reduces unemployment but worsens infant mortality rate. Electricity consumption reduces infant mortality rate. Hence, for the purpose of policy harmonization tailored toward improving well-being in the emerging economies of Africa, it is recommended that more of coal consumption and efficient use of electricity must be encouraged.


2010 ◽  
Vol 26 ◽  
pp. 85-99 ◽  
Author(s):  
Rosemary Black ◽  
Penny Davidson ◽  
Karen Retra

AbstractThis paper presents the results of a study that explored the effectiveness of three intervention strategies in facilitating energy saving behaviour among resident undergraduate university students. In contrast to a dominant practice of motivating with rewards or competition this study sought to appeal to students' intrinsic motivations. An experimental design was used with two intervention groups and a control group. The interventions were the provision of real-time feedback provided by an inhouse energy consumption display unit (ecoMeter) and a targeted social marketing approach. They were evaluated using energy consumption data and self-report data from the participants via an on-line survey and focus groups. Across the three research phases the rate of reduced electricity consumption for the interventions ranged from an average of 17% to 28% less than the control group. The findings provide evidence that facilitation of intrinsically motivated behaviours can result in reduced energy use and greenhouse gas emissions.


2018 ◽  
Vol 3 (1) ◽  
pp. 34-46
Author(s):  
Oyeleke Oluwaseun Oyerinde

Understanding locational variations in household energy consumption is critical to ascertaining dichotomies of energy use, need and wellbeing. In recognition of this, the study compares quantities of household energy consumption among urban, peri-urban and rural areas in Ibadan region, Nigeria using Net Heating Value (NHV). It employs a stratified random sampling of 166 households across the three zones. Results show that electricity, majorly used for appliances is dominant in the urban in contrast to fuelwood at the peri-urban and rural areas where cooking is the major end use. Though the quantities of total household energy consumption do not vary significantly at p < .05, electricity consumption is however significantly higher in urban households than in peri-urban and rural households. The Multiple Regression Analysis (MRA) and Analysis of Variance (ANOVA) indicate that socioeconomic characteristics significantly influenced the quantity of household energy consumption at the urban area only. Major variations between locations appear to be in energy types and end uses rather than quantity consumed.


2020 ◽  
Vol 52 (1) ◽  
pp. 1
Author(s):  
Prabang Setyono ◽  
Widhi Himawan ◽  
Cynthia Permata Sari ◽  
Totok Gunawan ◽  
Sigit Heru Murti

Considered as a trigger of climate change, greenhouse gas (GHG) is a global environmental issue. The City of Surakarta in Indonesia consists mainly of urban areas with high intensities of anthropogenic fossil energy consumption and, potentially, GHG emission. It is topographically a basin area and most likely prompts a Thermal Inversion, creating a risk of accumulation and entrapment of air pollutants or GHGs at low altitudes. Vegetation has been reported to mitigate the rate of increase in emissions because it acts as a natural carbon sink. This study aimed to mitigate the GHG emissions from energy consumption in Surakarta and formulate recommendations for control. It commenced with calculating the emission factors based on the IPCC formula and determining the key categories using the Level Assessment approach. It also involved computing the vegetation density according to the NDVI values of the interpretation of Sentinel 2A imagery. The estimation results showed that in 2018, the emission loads from the energy consumption in Surakarta reached 1,217,385.05 (tons of CO2e). The key categories of these emissions were electricity consumption, transportation on highways, and the domestic sector, with transportation on highways being the top priority. These loads have exceeded the local carrying capacity because they create an imbalance between emission and natural GHG sequestration by vegetations.


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