Using Building Integrated Photovoltaic Thermal (BIPV/T) Systems to Achieve Net Zero Goal: Current Trends and Future Perspectives

Sohani, Ali; Cornaro, Cristina; Shahverdian, Mohammad Hassan; Samiezadeh, Saman; Hoseinzadeh, Siamak; Pierro, Marco; Moser, David

doi:10.1007/978-3-031-15218-4_5

Cited by 5 publications

(2 citation statements)

References 37 publications

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“…ML has shown great potential for modeling for various complex tasks such as pattern recognition in domains ranging from computer vision [2] over speech recognition [3] and text understanding [4] to Game AI [5]. Additionally, due to the data-based nature from a huge number of examples, ML is being increasingly important and successful in other areas such as electrical engineering and electromagnetics (EM) [6]- [10].…”

Section: Introductionmentioning

confidence: 99%

Detection of Modal Numbers from Field Configurations in Rectangular Waveguides via Machine Learning Models of Noisy Datasets

Choupanzadeh,

Zadehgol

2024

Preprint

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We propose a machine learning (ML) modeling methodology to predict the propagation mode number of electromagnetic (EM) fields inside a metallic rectangular waveguide based on the field configuration in the waveguide cross-section, in the presence of noise. We consider the Transverse Electric (TEmn) modes and assume m and n in the range of 0 to 2 inside the waveguides, where the magnitude and phase of the noiseless field configurations are obtained from the analytical solution of the electric vector field E. We generate training/testing datasets that includes 64,000 plots of the magnitude and phase of E over the waveguide cross-section, spanning various TE modes in the frequency range of 13-17 GHz. Our methodology for training and evaluation is based on the classification model, and relies primarily on Stochastic Gradient Descent (SGD) and k-Nearest Neighbors. For real-world scenarios which include noise, we introduce two random distributions in the datasets; specifically, the exponential and the Gaussian distributions are added onto the computed E-fields to further challenge the ML model. We discuss the limitations of the proposed ML modeling approach and the challenges in finding the optimal ML model for these types of problems. The proposed methodology may be generalized to predict both the TE and Transverse Magnetic (TMmn) mode numbers with a wide ranges of m and n, as well as for other types of waveguides; e.g., circular, elliptical, etc.

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Section: Introductionmentioning

confidence: 99%

Detection of Modal Numbers from Field Configurations in Rectangular Waveguides via Machine Learning Models of Noisy Datasets

Choupanzadeh,

Zadehgol

2024

Preprint

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“…The growing world population of the world has led to an increasing need for more energy resources [1]. Today, one of the most popular types of energy in the world is electrical energy [2][3][4]. There are different alternatives for being converted into electricity.…”

Section: Introduction 1motivationmentioning

confidence: 99%

A Road Map to Detect the Foremost 3E Potential Areas for Installation of PV Façade Technology Using Multi-Criteria Decision Making

et al. 2022

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A procedure to prioritize the cities to utilize a building integrated photovoltaic thermal (BIPV/T) system is proposed in which the technique for order of preference by similarity to ideal solution (TOPSIS) is employed as a systematic decision-making method. Electricity generation and heat recovery in a year from the energy side, levelized cost of electricity (LCOE), and payback period (PBP) from the economic viewpoint, as well as the carbon dioxide savings from the environmental perspective, are taken into account as the decision criteria. They are the key economic, environmental, and energy (3E) performance indicators of the system. The novelty of the proposed research approach is two items. The first item is systematic and could be employed for each and every case. Moreover, another item is that selection is made based on energy, economic, and environmental (3E) criteria all together, as the important aspects of an energy system. Having introduced the procedure, it is utilized to rank five cities in Iran for the installation of BIPV/T technologies. The cities are Tehran, Tabriz, Yazd, Rasht, and Bandar Abbas, where each one is a populated city from one of the climatic conditions of the country. According to the results, a high priority is seen for two cities: the first city is Yazd with the highest ambient temperature and relative humidity among the alternatives, and the other city is Tehran, with the highest natural gas and electricity tariffs, as well as the greatest price for operating and maintenance. The values of heat recovery, electricity generation, carbon dioxide savings, PBP, and LCOE for Yazd are 42.3 MWh, 23.4 MWh, 16.8 tons, 5.48 years, and 9.45 cents per kWh. The corresponding values for Tehran are 35.6 MWh, 21.6 MWh, 15.0 tons, 2.79 years, and 8.71 cents per kWh, respectively.

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Modelling and Energy Analysis of a Solar Cooling System Powered by a Photovoltaic (PV) System for a Net-Zero Energy Building (NZEB) Using TRNSYS-PVsyst

Salehi Dezfouli,

Dehghani-Sanij,

Abdul Kadir

2024

Reducing the Effects of Climate Change Using Building-Integrated and Building-Applied Photovoltaics in the Power Supply

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Using Building Integrated Photovoltaic Thermal (BIPV/T) Systems to Achieve Net Zero Goal: Current Trends and Future Perspectives

Cited by 5 publications

References 37 publications

Detection of Modal Numbers from Field Configurations in Rectangular Waveguides via Machine Learning Models of Noisy Datasets

Detection of Modal Numbers from Field Configurations in Rectangular Waveguides via Machine Learning Models of Noisy Datasets

A Road Map to Detect the Foremost 3E Potential Areas for Installation of PV Façade Technology Using Multi-Criteria Decision Making

Modelling and Energy Analysis of a Solar Cooling System Powered by a Photovoltaic (PV) System for a Net-Zero Energy Building (NZEB) Using TRNSYS-PVsyst

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