Evaluating solar irradiance over facades in high building cities, based on LiDAR technology

Martínez-Rubio, A.; Adán, Félix Sanz; Peña, Jacinto Santamaría; Martinez, A.

doi:10.1016/j.apenergy.2016.08.163

Cited by 47 publications

(17 citation statements)

References 37 publications

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“…Currently, solar photovoltaic technology can reach grid parity with other energy sources in specific geographic areas (Martínez‐Rubio, Sanz‐Adan, Santamaría‐Peña, & Martínez, ), despite its low efficiency (between 10% and 20%, depending on the type of technology). As the rate of urbanization continues accelerating worldwide, the installation of solar energy panels in buildings plays an increasingly significant role in sustainable urban development.…”

Section: Lidar Backgroundmentioning

confidence: 99%

Estimation of forest biomass from light detection and ranging data by using machine learning

2019

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The use of data driven predictive systems is becoming widespread as innovations in machine learning techniques have allowed the training of increasingly sophisticated models via the available data. The light detection and ranging (LiDAR) remote sensing technique is being increasingly applied to obtain informative terrain maps, due to its ability to collect large amounts of data with satisfactory accuracy. This paper focuses on the application of machine‐learning‐based predictive systems for the extraction of biomass information from LiDAR data. Biomass information has inmense ecological and economical value. We demonstrate the estimation of the Pinus radiata biomass in the Arratia‐Nervión region (Spain). Biomass estimation is considered a regression problem in which the ground truth for some specific sample sites is available. The promising results obtained in this study indicate that LiDAR data can be used to carry out detailed biomass mappings by the extrapolation of the models trained in this study.

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Section: Lidar Backgroundmentioning

confidence: 99%

Estimation of forest biomass from light detection and ranging data by using machine learning

2019

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“…Diffuse radiation thus forms a significant part of the resource [20]. It should also be noted that obtaining the measure of radiation can be useful in other fields that require knowledge of this variable, such as meteorological studies related to climate change [21], analysis of passive solar systems, or agriculture, as well as programming irrigation systems or bioclimatic architecture, among many others [22,23].…”

Section: Obtaining Global Radiation In Photovoltaic Applications Prementioning

confidence: 99%

Development of a Utility Model for the Measurement of Global Radiation in Photovoltaic Applications in the Internet of Things (IoT)

et al. 2019

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In order to develop future projects in the field of photovoltaic solar energy, it is essential to accurately know the potential solar resources. There are many methods to estimate the incident solar radiation in a certain place. However, most of them are very expensive or do not have the ideal characteristics for good monitoring of a particular photovoltaic installation. For these reasons, an electronic device connected to the internet of things (IoT) is presented in this paper which manages to measure global radiation in photovoltaic applications. The device developed has been patented in the Spanish Patent and Trademark Office. It presents some features that make it very suitable to measure photovoltaic installations with the advantage of being a low cost and very reliable device. The device has been tested to determine global horizontal irradiance obtaining a correlation coefficient R2 = 0.994.

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“…Such initiative aims at fulfilling 20% of the overall energy needs of Europe with renewables by 2020 [4], and 27% by 2030 [5]. Considering that building-integrated installations have the advantage to preserve natural landscape with respect to large free-standing installations (PV farms), they have been promoted by local governments via specific policies, which led to a continuously increasing number of installations [6]. On the other hand, despite the outstanding potential in terms of solar energy and thus of sustainable electricity production [7], investments are still at the beginning in Africa [8].…”

Section: Introductionmentioning

confidence: 99%

“…(a) Percent error between the current model predictions of Equation(6) and the values extracted from the PVGIS tool for the yearly electrical energy output (Π y ) of FS PV systems over the 80 considered locations in Europe and Africa. Results are reported as a function of the latitude (φ) and longitude (λ), as well as in the form of (b) error distribution.…”

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confidence: 99%

Multiple-Regression Method for Fast Estimation of Solar Irradiation and Photovoltaic Energy Potentials over Europe and Africa

et al. 2018

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In recent years, various online tools and databases have been developed to assess the potential energy output of photovoltaic (PV) installations in different geographical areas. However, these tools generally provide a spatial resolution of a few kilometers and, for a systematic analysis at large scale, they require continuous querying of their online databases. In this article, we present a methodology for fast estimation of the yearly sum of global solar irradiation and PV energy yield over large-scale territories. The proposed method relies on a multiple-regression model including only well-known geodata, such as latitude, altitude above sea level and average ambient temperature. Therefore, it is particularly suitable for a fast, preliminary, offline estimation of solar PV output and to analyze possible investments in new installations. Application of the method to a random set of 80 geographical locations throughout Europe and Africa yields a mean absolute percent error of 4.4% for the estimate of solar irradiation (13.6% maximum percent error) and of 4.3% for the prediction of photovoltaic electricity production (14.8% maximum percent error for free-standing installations; 15.4% for building-integrated ones), which are consistent with the general accuracy provided by the reference tools for this application. Besides photovoltaic potentials, the proposed method could also find application in a wider range of installation assessments, such as in solar thermal energy or desalination plants.

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Evaluating solar irradiance over facades in high building cities, based on LiDAR technology

Cited by 47 publications

References 37 publications

Estimation of forest biomass from light detection and ranging data by using machine learning

Estimation of forest biomass from light detection and ranging data by using machine learning

Development of a Utility Model for the Measurement of Global Radiation in Photovoltaic Applications in the Internet of Things (IoT)

Multiple-Regression Method for Fast Estimation of Solar Irradiation and Photovoltaic Energy Potentials over Europe and Africa

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