Solar energy systems potential for nearly net zero energy residential buildings

Controlling air leakage through the building envelope remains a challenge, especially in light of the imperative to transition to a net-zero energy building sector and the increasing importance of indoor air quality during extreme weather events such as wildfires. The British Columbia Energy Step Code is a performance-based compliance option in the British Columbia Building Code which is intended to provide a transition pathway to net-zero energy ready construction by 2032. For small residential buildings, performance targets entail thresholds for the measured air leakage rate through the building envelope. This paper reports on the airtightness of the first 145 single- and two-family dwellings built under the Energy Step Code in Richmond, BC. Although the first phase of the implementation of the Energy Step Code in Richmond entailed no airtightness targets (only testing), results indicate improvement compared to the historical levels of airtightness in the region.

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The applicability of nearly/net zero energy residential buildings in Brazil – A study of a low standard dwelling in three different Brazilian climate zones

Indoor and Built Environment ◽

10.1177/1420326x20961156 ◽

2020 ◽

pp. 1420326X2096115

Author(s):

Jaime Resende ◽

Marta Monzón-Chavarrías ◽

Helena Corvacho

Keyword(s):

Energy Consumption ◽

Energy Balance ◽

Thermal Comfort ◽

Residential Buildings ◽

Performance Standard ◽

Single Family ◽

Zero Energy ◽

Climate Zones ◽

Net Zero Energy ◽

Net Zero

Buildings account for 34% of world energy consumption and about half of electricity consumption. The nearly/Net Zero Energy Building (nZEB/NZEB) concepts are regarded as solutions for minimizing this problem. The countries of Southern Europe, which included the nZEB concept recently in their regulatory requirements, have both heating and cooling needs, which adds complexity to the problem. Brazil may benefit from their experience since most of the Brazilian climate zones present significant similarities to the Southern European climate. Brazil recently presented a household energy consumption increase, and a growing trend in the use of air conditioning is predicted for the coming decades. Simulations with various wall and roof solutions following the Brazilian Performance Standard were carried out in a low standard single-family house in three different climate zones in order to evaluate thermal comfort conditions and energy needs. Results show that in milder climate zones, achieving thermal comfort with a low energy consumption is possible, and there is a great potential to achieve a net zero-energy balance. In the extreme hot climate zone, a high cooling energy consumption is needed to provide thermal comfort, and the implementation of a nearly zero-energy balance may be more feasible.

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Assessment of Photovoltaic Capabilities in Urban Environments: A Case Study of Islamabad, Pakistan

Journal of Solar Energy Engineering ◽

10.1115/1.4046947 ◽

2020 ◽

Vol 142 (6) ◽

Cited By ~ 1

Author(s):

Muhammad Zubair ◽

Sajid Ghuffar ◽

Muhammad Shoaib ◽

Ahmed Bilal Awan ◽

Abdul Rauf Bhatti

Keyword(s):

Deep Learning ◽

Solar Energy ◽

Urban Areas ◽

Capital City ◽

Coefficient Of Performance ◽

Energy Yield ◽

Cooling Load ◽

Zero Energy ◽

Net Zero Energy ◽

Net Zero

Abstract Photovoltaic (PV) estimation in an urban environment requires detection of rooftop area, design of PV system based on optimization on PV placement distance and the study of additional benefit of lower cooling load of building by shading provided by PV panels. The study is aimed at policymakers to introduce renewable energy policy toward net-zero energy buildings in urban areas. In this research, the capital city of Pakistan, Islamabad, is analyzed for rooftop PV capabilities using deep learning algorithms. The area of the rooftop is calculated by extracting buildings in high-resolution satellite imagery using a deep learning algorithm. The site location is analyzed for available solar energy resources. The distance between the rooftop-PV array is optimized based on self-shading losses, coefficient of performance, energy yield, net-zero energy analysis, and reduction of cooling load of the building provided by PV arrays as shading devices. The 40-km2 area of Islamabad considered in this research can generate 1038 GWh of solar energy annually from its 4.3-km2 rooftop area by installed capacity of 447 MW PV panels rows placed at 0.75 m apart. The electricity generated by Islamabad can curtail residential load from the national grid and form a near net-zero energy zone while the electrical energy from the grid can be provided to the industries to enhance the economy and reduce unemployment in Pakistan.

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flEECe, an energy use and occupant behavior dataset for net-zero energy affordable senior residential buildings

Scientific Data ◽

10.1038/s41597-019-0275-3 ◽

2019 ◽

Vol 6 (1) ◽

Cited By ~ 3

Author(s):

Frederick Paige ◽

Philip Agee ◽

Farrokh Jazizadeh

Keyword(s):

Affordable Housing ◽

Energy Use ◽

Residential Buildings ◽

Building Energy ◽

Hot Water ◽

Occupant Behavior ◽

Zero Energy ◽

Net Zero Energy ◽

Net Zero ◽

Building Energy Use

AbstractThe behaviors of building occupants have continued to perplex scholars for years in our attempts to develop models for energy efficient housing. Building simulations, project delivery approaches, policies, and more have fell short of their optimistic goals due to the complexity of human behavior. As a part of a multiphase longitudinal affordable housing study, this dataset represents energy and occupant behavior attributes for 6 affordable housing units over nine months in Virginia, USA which are not performing to the net-zero energy standard they were designed for. This dataset provides researchers the ability to analyze the following variables: energy performance, occupant behaviors, energy literacy, and ecological perceptions. Energy data is provided at a 1 Hz sampling rate for four circuits: main, hot water heater, dryer, and HVAC. Building specifications, occupancy, weather data, and neighboring building energy use data are provided to add depth to the dataset. This dataset can be used to update building energy use models, predictive maintenance, policy frameworks, construction risk models, economic models, and more.

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