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Photogrammetry and deep learning for energy production prediction and building-integrated photovoltaics decarbonization

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Abstract

Building-Integrated photovoltaics (BIPV) have emerged as a promising sustainable energy solution, relying on accurate energy production predictions and effective decarbonization strategies for efficient deployment. This paper presents a novel approach that combines photogrammetry and deep learning techniques to address the problem of BIPV decarbonization. The method is called BIM-AITIZATION referring to the integration of BIM data, AI techniques, and automation principles. It integrates photogrammetric data into practical BIM parameters. In addition, it enhances the precision and reliability of PV energy prediction by using artificial intelligence strategies. The primary aim of this approach is to offer advanced, data-driven energy forecasts and BIPV decarbonization while fully automating the underlying process. To achieve this, the first step is to capture point cloud data of the building through photogrammetric acquisition. This data undergoes preprocessing to identify and remove unwanted points, followed by plan segmentation to extract the plan facade. After that, a meteorological dataset is assembled, incorporating various attributes that influence energy production, including solar irradiance parameters as well as BIM parameters. Finally, machine and deep learning techniques are used for accurate photovoltaic energy predictions and the automation of the entire process. Extensive experiments are conducted, including multiple tests aimed at assessing the performance of diverse machine learning models. The objective is to identify the most suitable model for our specific application. Furthermore, a comparative analysis is undertaken, comparing the performance of the proposed model against that of various established BIPV software tools. The outcomes reveal that the proposed approach surpasses existing software solutions in both accuracy and precision. To extend its applicability, the approach is evaluated using a building case study, demonstrating its ability to generalize effectively to new building data.

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Abbreviations

AI:

artificial intelligence

ANN:

artificial neural network

BHI:

beam irradiation on horizontal plane at ground level (Wh/m2)

BIM SurfaceSUD Facade:

surface of the south facade (m2)

BIM X:

X coordinate (m)

BIM Y:

Y coordinate (m)

BIM Z:

Z coordinate (m)

CAMS:

Copernicus Atmosphere Monitoring Service

CNN:

convolutional neural network

DHI:

diffused horizontal irradiance (Wh/m2)

DNI:

diffused normal irradiance (Wh/m2)

EV:

explained variance

GHI:

global horizontal irradiance (Wh/m2)

KNN:

K-nearest neighbors

LaGC:

latitude, longitude, altitude geographical coordinates (DMS)

LR:

linear regression

MLESAC:

Maximum Likelihood Estimation SAmple Consensus

MSE:

mean squared error

NSRD:

National Solar Radiation Database

P :

pressure (mbar)

PV-24H:

pressure variation in 24 hours (mbar)

PVGIS:

photovoltaic geographic information system

PW:

precipitate water (cm)

RANSAC:

RANdom SAmple Consensus

ReLU:

rectified linear units

RF:

random forest

RH:

relative humidity (%)

SIM:

sunshine irradiance measured (kWh/m2)

SVR:

support vector regression

SZA:

solar zenith angle (degree)

T :

external air temperature (°C)

TOA:

irradiation on horizontal plane at the top of atmosphere (Wh/m2)

WD:

wind direction (degree)

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Acknowledgements

This work was supported by CESI EST and the GRAND EST region. The authors are very grateful to Mourad ZGHAL for fruitful discussions and Benoit DESTENAY (Teacher & responsible in charge of education at CESI school of engineering), Pierre BALLESTER, Cemal OCAKTAN, Oussama OUSSOUS and SOW Mame-Cheikh for technical assistance. The authors are grateful to GBAGUIDI HAORE Sevi (Teacher & responsible in charge of education at CESI school of engineering) and energy expert for his excellent technical support on the subject of the energy decarbonization of buildings. We would like to thank Ophéa-Eurométropole Habitat Strasbourg for allowing us to have the energy production data for these buildings.

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Authors and Affiliations

  1. CESI LINEACT, 7 bis Av. Robert Schuman, 51100, Reims, France

    Ilyass Abouelaziz

  2. CESI LINEACT, Parc Club des Tanneries 2 all Foulons, 67380, Strasbourg, France

    Youssef Jouane

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  1. Ilyass Abouelaziz
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  2. Youssef Jouane
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Ilyass Abouelaziz and Youssef Jouane. The first draft of the manuscript was written by Youssef Jouane and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Youssef Jouane.

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Abouelaziz, I., Jouane, Y. Photogrammetry and deep learning for energy production prediction and building-integrated photovoltaics decarbonization. Build. Simul. 17, 189–205 (2024). https://doi.org/10.1007/s12273-023-1089-y

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