Case study of personal heaters in a Plus energy building – Simulations of potential energy savings and results from a field test

This article outlines a socio-political theory appropriate for the study of the ecological repercussions of contemporary media technologies. More specifically, this approach provides a means of assessing the material impacts of media technologies and the representations of capitalist ecological crises. This approach builds on the work of ecological economists, ecosocialist scholars, and Marx’s writings on the conditions of production to argue that capitalism necessarily results in ecological destabilization. Taking Apple’s 2016 Environmental Responsibility Report as a case study, the article uses the theory to analyze Apple’s responses to ecological crises. The article asserts that Apple’s reactions are emblematic of the capitalist compulsion for increasing rates of productivity. However, unless the matter/energy savings achieved through higher rates of productivity surpass the overall increase in the flow of matter/energy in production, ecological crises will continue. Ultimately, capital accumulation ensures continued ecological destabilization.

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Evaluation of bioclimatic potential, energy consumption, CO2-emission, and life cycle cost of a residential building located in Sub-Saharan Africa; a case study of eight countries

Solar Energy ◽

10.1016/j.solener.2021.02.052 ◽

2021 ◽

Vol 218 ◽

pp. 512-524

Author(s):

Modeste Kameni Nematchoua ◽

Sigrid Reiter

Keyword(s):

Life Cycle ◽

Energy Consumption ◽

Potential Energy ◽

Life Cycle Cost ◽

Co2 Emission ◽

Residential Building ◽

Sub Saharan Africa ◽

Sub Saharan

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Evaluation methods of the daylight performance and potential energy saving of tubular daylight guide systems: A review

Indoor and Built Environment ◽

10.1177/1420326x21992419 ◽

2021 ◽

pp. 1420326X2199241

Author(s):

Hanlin Li ◽

Dan Wu ◽

Yanping Yuan ◽

Lijun Zuo

Keyword(s):

Potential Energy ◽

Energy Saving ◽

Energy Savings ◽

Performance Metrics ◽

Field Measurements ◽

Building Design ◽

Estimation Methods ◽

The Past ◽

Potential Energy Saving ◽

Daylight Performance

In the past 30 years, tubular daylight guide systems (TDGSs) have become one of the most popular ways to transport outdoor natural light into the inner space in building design. However, tubular daylight guide systems are not widely used because of the lack of methods to evaluate methods on the suitability of the TDGSs. This study therefore summarizes the daylight performance metrics of TDGSs and presents the estimation methods in terms of field measurements, simulation and empirical formulae. This study focuses on the daylight performance and potential energy savings of TDGSs. Moreover, this study will be helpful for building designers to build healthy, comfortable and energy-saving indoor environment.

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A Computer Vision-Based Occupancy and Equipment Usage Detection Approach for Reducing Building Energy Demand

Energies ◽

10.3390/en14010156 ◽

2020 ◽

Vol 14 (1) ◽

pp. 156

Author(s):

Paige Wenbin Tien ◽

Shuangyu Wei ◽

John Calautit

Keyword(s):

Deep Learning ◽

Real Time ◽

Energy Demand ◽

Energy Savings ◽

Internal Heat ◽

Electrical Equipment ◽

Building Energy ◽

Hvac Systems ◽

Equipment Usage

Because of extensive variations in occupancy patterns around office space environments and their use of electrical equipment, accurate occupants’ behaviour detection is valuable for reducing the building energy demand and carbon emissions. Using the collected occupancy information, building energy management system can automatically adjust the operation of heating, ventilation and air-conditioning (HVAC) systems to meet the actual demands in different conditioned spaces in real-time. Existing and commonly used ‘fixed’ schedules for HVAC systems are not sufficient and cannot adjust based on the dynamic changes in building environments. This study proposes a vision-based occupancy and equipment usage detection method based on deep learning for demand-driven control systems. A model based on region-based convolutional neural network (R-CNN) was developed, trained and deployed to a camera for real-time detection of occupancy activities and equipment usage. Experiments tests within a case study office room suggested an overall accuracy of 97.32% and 80.80%. In order to predict the energy savings that can be attained using the proposed approach, the case study building was simulated. The simulation results revealed that the heat gains could be over or under predicted when using static or fixed profiles. Based on the set conditions, the equipment and occupancy gains were 65.75% and 32.74% lower when using the deep learning approach. Overall, the study showed the capabilities of the proposed approach in detecting and recognising multiple occupants’ activities and equipment usage and providing an alternative to estimate the internal heat emissions.

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