scholarly journals Our building is smarter than your building: The use of competitive rivalry to reduce energy consumption and linked carbon footprint

2014 ◽  
pp. 464-471
Keyword(s):  
Energy Consumption ◽  
Development Strategy ◽  
Carbon Footprint ◽  
Electricity Consumption ◽  
Pilot Project ◽  
Smart Buildings ◽  
Environmental Objectives ◽  
Global University ◽  
Intelligent Community ◽  
Research Stream

This research is located within the smart city discourse and explores the linkage between smart buildings and an intelligent community, employing the University of Cape Town as a case study. It is also situated within the research stream of Green Information Systems, which examines the confluence between technology, people, data and processes, in order to achieve environmental objectives such as reduced energy consumption and its associated carbon footprint. Since approximately 80% of a university’s carbon footprint may be attributed to electricity consumption and as the portion of energy used inefficiently by buildings is estimated at 33% an argument may be made for seeing a campus as a “living laboratory” for energy consumption experiments in smart buildings. Integrated analytics were used to measure, monitor and mitigate energy consumption, directly linked to carbon footprinting. This paper examines a pilot project to reduce electricity consumption through a smart building competition. The lens used for this research was the empirical framework provided by the International Sustainable Campus Network/Global University Leadership Forum Charter. Preliminary findings suggest a link between the monitoring of smart buildings and behaviour by a segment of the intelligent community in the pursuit of a Sustainable Development strategy.

Carbon footprint of the University of Cape Town

2011 ◽  
Vol 22 (2) ◽  
pp. 2-12 ◽  
Author(s):  
Thapelo C.M. Letete ◽  
Nothando Wandile Mungwe ◽  
Mondli Guma ◽  
Andrew Marquard
Keyword(s):  
Energy Consumption ◽  
Carbon Footprint ◽  
Action Plan ◽  
Electricity Consumption ◽  
Cape Town ◽  
Emission Mitigation ◽  
Goods And Services ◽  
Per Capita ◽  
University Of Cape Town ◽  
The University

Since signing the Talloires Declaration in 1990, the University of Cape Town (UCT) has been striving to set an example of environmental responsibility by establishing environmentally sound policies and practices, and by developing curricula and research initiatives to support an environmentally sustainable future. One of the most recent efforts in this quest was the release of a Green Campus Action Plan for the University of Cape Town by the Properties and Services Department in 2008. While the Plan proposed a number of carbon emission mitigation interventions for the University, it also stressed the need to conduct a detailed and comprehensive carbon footprint analysis for the whole University. The aim of this analysis was to determine the carbon footprint of UCT, not only to give a tangible number with which the University’s carbon sustainability level can be compared with other academic institutions, but also to provide the much needed baseline against which future mitigation efforts on the university campus can be measured. UCT’s carbon footprint for the year 2007 was found to be about 83 400 tons CO2-eq, with campus energy consumption, Transportation and Goods and Services contributing about 81%, 18% and 1% the footprint respectively. Electricity consumption alone contributes about 80% of all the emissions associated with university activities. UCT’s per-capita emissions for 2007 amount to about 4.0 tons CO2-eq emissions per student. For comparison only, South Africa’s 2007 per capita emissions were estimated at 10.4 tons CO2-eq. In terms of energy consumption only, UCT’s footprint is about 3.2 tons CO2-eq per student, higher than the National University of Lesotho’s value of 0.1 and much lower than Massachusetts Institute of Technology’s value of 33.1.

scholarly journals Regional Diversification of Electricity Consumption in Rural Areas of Poland

Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8532
Author(s):  
Paweł Siemiński ◽  
Jakub Hadyński ◽  
Jarosław Lira ◽  
Anna Rosa
Keyword(s):  
Energy Consumption ◽  
Development Strategy ◽  
Electricity Market ◽  
Rural Areas ◽  
Urban Areas ◽  
Electricity Consumption ◽  
Data Bank ◽  
Rate Of Change ◽  
Local Data ◽  
Unit Energy

Access to energy, including electricity, determines countries’ socio-economic development. The growing demand for electricity translates into environmental problems. Energy is therefore a crucial element of the European Union’s sustainable development strategy. This article aims to present the changes taking place in the electricity market in Poland considering the goals of the energy policy until 2040. This is the basis for the determination of the scale of processes taking place in the Polish energy sector from two perspectives, i.e., the production of electricity considering its level and energy carriers used, and the consumption of electricity in households depending on their location (rural vs. urban areas). The research was conducted at the regional level (NUTS 2 until 2017) in Poland. Secondary data from the Central Statistical Office (GUS) contained in the Local Data Bank were used, along with information from the European Commission and Eurostat websites. Results of the study made it possible to identify areas in which a greater environmental load is observed due to increasing electricity consumption. The coefficient of localization and concentration (by Florence) and the rate of change were applied. These results indicate that, in Poland, it is now the rural areas that have a greater negative environmental impact than urban areas, resulting from differences in unit energy consumption. Compared to the other provinces, rural areas of Podlaskie province had the highest rate of growth in energy consumption in the years 2004–2019, with an annual average of almost 20%.

CLOUD BASED INTERNET OF THINGS FOR SMART CONNECTED OBJECTS

2019 ◽  
Vol 01 (02) ◽  
pp. 31-39 ◽  
Author(s):  
Duraipandian M. ◽  
Vinothkanna R.
Keyword(s):  
Energy Consumption ◽  
Internet Of Things ◽  
Smart Home ◽  
Electricity Consumption ◽  
Considerable Improvement ◽  
The Internet ◽  
The Internet Of Things ◽  
At Home

The paper proposing the cloud based internet of things for the smart connected objects, concentrates on developing a smart home utilizing the internet of things, by providing the embedded labeling for all the tangible things at home and enabling them to be connected through the internet. The smart home proposed in the paper concentrates on the steps in reducing the electricity consumption of the appliances at the home by converting them into the smart connected objects using the cloud based internet of things and also concentrates on protecting the house from the theft and the robbery. The proposed smart home by turning the ordinary tangible objects into the smart connected objects shows considerable improvement in the energy consumption and the security provision.

scholarly journals Life-Cycle Assessment of Carbon Footprint of Bike-Share and Bus Systems in Campus Transit

2020 ◽  
Vol 13 (1) ◽  
pp. 158
Author(s):  
Sishen Wang ◽  
Hao Wang ◽  
Pengyu Xie ◽  
Xiaodan Chen
Keyword(s):  
Life Cycle Assessment ◽  
Life Cycle ◽  
Energy Consumption ◽  
Carbon Footprint ◽  
Co2 Emission ◽  
Raw Material ◽  
Transit System ◽  
Two Systems ◽  
Bus System ◽  
Bike Share

Low-carbon transport system is desired for sustainable cities. The study aims to compare carbon footprint of two transportation modes in campus transit, bus and bike-share systems, using life-cycle assessment (LCA). A case study was conducted for the four-campus (College Ave, Cook/Douglass, Busch, Livingston) transit system at Rutgers University (New Brunswick, NJ). The life-cycle of two systems were disaggregated into four stages, namely, raw material acquisition and manufacture, transportation, operation and maintenance, and end-of-life. Three uncertain factors—fossil fuel type, number of bikes provided, and bus ridership—were set as variables for sensitivity analysis. Normalization method was used in two impact categories to analyze and compare environmental impacts. The results show that the majority of CO2 emission and energy consumption comes from the raw material stage (extraction and upstream production) of the bike-share system and the operation stage of the campus bus system. The CO2 emission and energy consumption of the current campus bus system are 46 and 13 times of that of the proposed bike-share system, respectively. Three uncertain factors can influence the results: (1) biodiesel can significantly reduce CO2 emission and energy consumption of the current campus bus system; (2) the increased number of bikes increases CO2 emission of the bike-share system; (3) the increase of bus ridership may result in similar impact between two systems. Finally, an alternative hybrid transit system is proposed that uses campus buses to connect four campuses and creates a bike-share system to satisfy travel demands within each campus. The hybrid system reaches the most environmentally friendly state when 70% passenger-miles provided by campus bus and 30% by bike-share system. Further research is needed to consider the uncertainty of biking behavior and travel choice in LCA. Applicable recommendations include increasing ridership of campus buses and building a bike-share in campus to support the current campus bus system. Other strategies such as increasing parking fees and improving biking environment can also be implemented to reduce automobile usage and encourage biking behavior.

scholarly journals Temperature Effects on Electricity and Gas Consumption: Empirical Evidence from Mexico and Projections under Future Climate Conditions

2020 ◽  
Vol 13 (1) ◽  
pp. 305
Author(s):  
W.J. Wouter Botzen ◽  
Tim Nees ◽  
Francisco Estrada
Keyword(s):  
Climate Change ◽  
Energy Consumption ◽  
Fixed Effects ◽  
Electricity Consumption ◽  
Energy Transition ◽  
Net Energy ◽  
Economic Costs ◽  
Climate Conditions ◽  
Linear Relationships ◽  
Gas Consumption

Fixed effects panel models are used to estimate how the electricity and gas consumption of various sectors and residents relate to temperature in Mexico, while controlling for the effects of income, manufacturing output per capita, electricity and gas prices and household size. We find non-linear relationships between energy consumption and temperature, which are heterogeneous per state. Electricity consumption increases with temperature, and this effect is stronger in warm states. Liquified petroleum gas consumption declines with temperature, and this effect is slightly stronger in cold states. Extrapolations of electricity and gas consumption under a high warming scenario reveal that electricity consumption by the end of the century for Mexico increases by 12%, while gas consumption declines with 10%, resulting in substantial net economic costs of 43 billion pesos per year. The increase in net energy consumption implies greater efforts to comply with the mitigation commitments of Mexico and requires a much faster energy transition and substantial improvements in energy efficiency. The results suggest that challenges posed by climate change also provide important opportunities for advancing social sustainability goals and the 2030 Agenda for Sustainable Development. This study is part of Mexico’s Sixth National Communication to the United Nations Framework Convention on Climate Change.

scholarly journals Decarbonization of Residential Building Energy Supply: Impact of Cogeneration System Performance on Energy, Environment, and Economics

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2538
Author(s):  
Praveen K. Cheekatamarla
Keyword(s):  
Carbon Dioxide ◽  
Power Generation ◽  
Energy Consumption ◽  
Carbon Footprint ◽  
Residential Buildings ◽  
Building Energy ◽  
Primary Energy ◽  
Primary Energy Consumption ◽  
Cogeneration System ◽  
The Impact

Electrical and thermal loads of residential buildings present a unique opportunity for onsite power generation, and concomitant thermal energy generation, storage, and utilization, to decrease primary energy consumption and carbon dioxide intensity. This approach also improves resiliency and ability to address peak load burden effectively. Demand response programs and grid-interactive buildings are also essential to meet the energy needs of the 21st century while addressing climate impact. Given the significance of the scale of building energy consumption, this study investigates how cogeneration systems influence the primary energy consumption and carbon footprint in residential buildings. The impact of onsite power generation capacity, its electrical and thermal efficiency, and its cost, on total primary energy consumption, equivalent carbon dioxide emissions, operating expenditure, and, most importantly, thermal and electrical energy balance, is presented. The conditions at which a cogeneration approach loses its advantage as an energy efficient residential resource are identified as a function of electrical grid’s carbon footprint and primary energy efficiency. Compared to a heat pump heating system with a coefficient of performance (COP) of three, a 0.5 kW cogeneration system with 40% electrical efficiency is shown to lose its environmental benefit if the electrical grid’s carbon dioxide intensity falls below 0.4 kg CO2 per kWh electricity.

scholarly journals Investigating Primary Factors Affecting Electricity Consumption in Non-Residential Buildings Using a Data-Driven Approach

Energies ◽  
2019 ◽  
Vol 12 (21) ◽  
pp. 4046 ◽  
Author(s):  
Sooyoun Cho ◽  
Jeehang Lee ◽  
Jumi Baek ◽  
Gi-Seok Kim ◽  
Seung-Bok Leigh
Keyword(s):  
Energy Consumption ◽  
Energy Use ◽  
Simulation Analysis ◽  
Residential Buildings ◽  
Electric Energy ◽  
Electricity Consumption ◽  
Building Energy ◽  
Major Effect ◽  
Measuring Instruments ◽  
Building Energy Consumption

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.

An Efficient Decision-Making Approach for Short Term Indoor Room Temperature Forecasting in Smart Environment: Evidence from India

2021 ◽  
pp. 1-42
Author(s):  
Kamal Pandey ◽  
Bhaskar Basu ◽  
Sandipan Karmakar
Keyword(s):  
Decision Making ◽  
Energy Consumption ◽  
Room Temperature ◽  
Linear Models ◽  
Comfort Level ◽  
Support Vector ◽  
Nonlinear Phenomenon ◽  
Short Term ◽  
Smart Buildings ◽  
Temperature Forecasting

“Smart cities” start with “Smart Buildings” that improve the quality of urban services while ensuring sustainability. The current scenario in India reveals that the corporate and residential building structures are incorporating various self-sustainable techniques. Out of the multiple factors governing the comfort of smart buildings, indoor room temperature is an important one, since it drives the need of cooling or heating through controlling systems. Around one-third of total energy consumption of commercial buildings in India is attributed to Heating, Ventilation and Air Conditioning (HVAC) systems. Accurate prediction of indoor room temperature helps in creating an efficient equilibrium between energy consumption and comfort level of the building, thus providing opportunities for efficient decision making for energy optimization. Considering Indian climatic and geographical conditions, this paper proposes an efficient decision making approach using Bayesian Dynamic Models (BDM) for short-term indoor room temperature forecasting of a corporate building structure. The results obtained from Bayesian Dynamic linear model, using Expectation Maximization (EM) algorithm, have been compared to standard Auto Regressive Integrated Moving Average (ARIMA) model, and have been found to be more accurate. Forecasting of indoor room temperature is a highly nonlinear phenomenon, so to further improve the accuracy of the linear models, a hybrid modeling approach has been proposed. The inclusion of state-of-the-art nonlinear models such as Artificial Neural Networks (ANNs) and Support Vector Regression (SVR) improves the forecasting accuracy of the linear models significantly. Results show that the hybrid model obtained using BDM and ANN is the best fit model.

Energy consumption and carbon footprint accounting of urban and rural residents in Beijing through Consumer Lifestyle Approach

2019 ◽  
Vol 98 ◽  
pp. 575-586 ◽  
Author(s):  
Caocao Chen ◽  
Gengyuan Liu ◽  
Fanxin Meng ◽  
Yan Hao ◽  
Yan Zhang ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Carbon Footprint ◽  
Rural Residents ◽  
Urban And Rural Residents
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