How Much Building Renewable Energy Is Enough? The Vertical City Weather Generator (VCWG v1.4.4)

Aliabadi, Amir A.; Moradi, Mohsen; McLeod, Rachel M.; Calder, David; Dernovsek, Robert

doi:10.3390/atmos12070882

Cited by 14 publications

(2 citation statements)

References 53 publications

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“…A popular open-source energy simulation tool is EP, which was developed by the US Department of Energy [17]. The EP weather file (EPW) structure is also used by other simulation tools, such as IES, TAS, and VCWG, making it a valuable source in the energy simulation industry [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Development of the Indian Future Weather File Generator Based on Representative Concentration Pathways

et al. 2022

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India’s fossil-fuel-based energy dependency is up to 68%, with the commercial and residential sectors contributing to the rise of building energy demand, energy use, and greenhouse gas emissions. Several studies have shown that the increasing building energy demand is associated with increased space-cooling ownership and building footprint. The energy demand is predicted to grow further with the conditions of global warming and the phenomenon of urban heat islands. Building designers have been using state-of-the-art transient simulation tools to evaluate energy-efficient envelopes with present-day weather files that are generated with historical weather datasets for any specific location. Designing buildings with historical climatic conditions makes the buildings vulnerable to the predicted climate change impacts. In this paper, a weather file generator was developed to generate Indian future weather files using a geo-filtering-based spatial technique, as well as the temporal downscaling and machine learning (ML)-based bias correction approach proposed by Belcher et al. The future weather files of the three representative concentration pathways of 2.6, 4.5, and 8.5 could be generated for the years 2030, 2050, 2070, 2090, and 2100. Currently, the outputs of the second-generation Canadian Earth System Model are being used to create future weather files that will aid architects, urban designers, and planners in developing a built environment that is resilient to climate change. The novelty lies in using observed historical data from present-day weather files on the typical meteorological year for testing and training ML models. The typical meteorological weather files are composed of the concatenation of the monthly weather datasets from different years, which are referred to for testing and training ML models for bias correction.

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Section: Introductionmentioning

confidence: 99%

Development of the Indian Future Weather File Generator Based on Representative Concentration Pathways

et al. 2022

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“…Among them, heating, ventilation, and air conditioning systems account for the majority of building energy consumption. Because the building energy prediction problem is a multivariate time prediction problem, 2 of 21 accurately describing the energy consumption in a building is very complicated [4][5][6]. Conversely, energy consumption in a building depends upon many factors, such as weather conditions, thermal characteristics of the building envelope, occupancy behavior, and performance of the underlying components.…”

Section: Introductionmentioning

confidence: 99%

Electricity Consumption Forecast of High-Rise Office Buildings Based on the Long Short-Term Memory Method

Lin

Wang

et al. 2021

Energies

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Various algorithms predominantly use data-driven methods for forecasting building electricity consumption. Among them, algorithms that use deep learning methods and, long and short-term memory (LSTM) have shown strong prediction accuracy in numerous fields. However, the LSTM algorithm still has certain limitations, e.g., the accuracy of forecasting the building air conditioning power consumption was not very high. To explore ways of improving the prediction accuracy, this study selects a high-rise office building in Shanghai to predict the air conditioning power consumption and lighting power consumption, respectively and discusses the influence of weather parameters and schedule parameters on the prediction accuracy. The results demonstrate that using the LSTM algorithm to accurately predict the electricity consumption of air conditioners is more challenging than predicting lighting electricity consumption. To improve the prediction accuracy of air conditioning power consumption, two parameters, relative humidity, and scheduling, must be added to the prediction model.

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Climate Zone-Based Energy Retrofits—Residential Buildings in Canada

McLeod,

Moradi,

Aliabadi

2023

Environmental Science and Engineering

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How Much Building Renewable Energy Is Enough? The Vertical City Weather Generator (VCWG v1.4.4)

Cited by 14 publications

References 53 publications

Development of the Indian Future Weather File Generator Based on Representative Concentration Pathways

Development of the Indian Future Weather File Generator Based on Representative Concentration Pathways

Electricity Consumption Forecast of High-Rise Office Buildings Based on the Long Short-Term Memory Method

Climate Zone-Based Energy Retrofits—Residential Buildings in Canada

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