Artificial neural networks for the generation of geothermal maps of ground temperature at various depths by considering land configuration

Kalogirou, Soteris A.; Florides, Georgios A.; Pouloupatis, Panayiotis; Panayides, I.; Joseph-Stylianou, Josephina; Zomeni, Zomenia

doi:10.1016/j.energy.2012.06.045

Cited by 40 publications

(15 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The idea was that if the temperature was an important parameter, to use the actual measurements of temperature and the predictions of a previous work [11] to improve the mapping capability of the ANN. This, however, gave a poorer performance with a correlation coefficient of only 0.9509.…”

Section: Methodsmentioning

confidence: 99%

“…Some of them also used other specialized software rather than ANNs for the construction of geothermal resource maps. Kalogirou et al [11] used ANNs for the generation of geothermal maps (contours) of temperature at three depths (20, 50 and 100 m) by considering land configuration in Cyprus while Kaftan et al [12] used ANNs to estimate the structure parameters as location, depth, and density contrasts for gravity data in Turkey. Xue et al [13] predicted the ground thermal conductivity by using samples from the Quaternary stratum in Tianjin.…”

Section: Introductionmentioning

confidence: 99%

“…Neural networks have been used in the past by Kalogirou et al [14] for time series reconstruction of precipitation records with acceptable accuracy. They have also been used in the drawing of isohyets, which are contour lines of equal rainfall [15]. Review of applications of ANNs in energy systems can be found in Refs.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Artificial neural networks for the generation of a conductivity map of the ground

et al. 2015

Self Cite

View full text Add to dashboard Cite

a b s t r a c tIn this paper a neural network is used for the generation of a contour map of the ground conductivity in Cyprus. Archived data of thermal conductivity of ground recorded at 41 boreholes are used for training a multiple hidden layer neural network with feedforward architecture. The correlation coefficient obtained between the predicted and training data set is 0.9657, indicating an accurate mapping of the data. The validation of the network was performed using an unknown dataset. The correlation coefficient for the unknown cases was 0.9553. In order to broaden the database, the patterns used for the validation of the technique were embedded into the training data set and a new training of the network was performed. The correlation coefficient value for this case was equal to 0.9718. A 10 Â 10 km grid is then drawn over a detailed topographic map of Cyprus and the various input parameters were recorded for each grid point. This information was then supplied to the trained network and by doing so ground conductivity was predicted at each grid-point. This map will be a helpful tool for engineers in designing geothermal heat pump systems in Cyprus.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Artificial neural networks for the generation of a conductivity map of the ground

et al. 2015

Self Cite

View full text Add to dashboard Cite

show abstract

“…Kalogirou et al [214] implemented an ANN, similar to a previous study [212], for the estimation of ground thermal conductivity in Cyprus. The estimated information was then used to generate geothermal maps for conductivity for the first 100 m in dry soil.…”

Section: Thermal Conductivitymentioning

confidence: 99%

Estimation of renewable energy and built environment-related variables using neural networks – A review

Rodrigues

Gomes

Gaspar

et al. 2018

Renewable and Sustainable Energy Reviews

View full text Add to dashboard Cite

This paper presents a review on the application of neural networks for the estimation, forecasting, monitoring, and classification of exogenous environmental variables that affect the performance, salubrity, and security of cities, buildings, and infrastructures. The forecast of these variables allows to explore renewable energy and water resources, to prevent potentially hazardous construction locations, and to find the healthiest places, thus promoting a more sustainable future. Five research themes are covered-solar, atmospheric, hydrologic, geologic, and climate change. The solar section comprises solar radiation, direct and diffuse radiation, infrared and ultraviolet radiation, clearness index, and sky luminance and luminous efficacy. The atmospheric section reviews wind, temperature, humidity, cloud classification, and storm prediction. The hydrologic section focuses on precipitation, rainfall-runoff, hail, snow, drought, flood, tides, water levels, and other variables. The geologic section covers works on landslides, earthquakes, liquefaction, erosion, soil classification, soil mechanics, and other properties. Finally, climate change forecasting and downscaling of climate models are reviewed. This work demonstrates the wide range of applications of these methods in different research fields. Some research gaps and interdisciplinary research opportunities are identified for future development of comprehensive forecast and evaluation approaches regarding the estimation of renewable energy and built environment-related variables.

show abstract

“…In [52] authors are analysing basic and dual-pressure ORC with the implementation done on a specific geothermal plant, as s case study. One of the prerequisite of geothermal energy utilization is the creation of quality geothermal maps, which was the focus of [53], where neural networks were used to determine potentials at three different depths. Beside electricity generation, geothermal energy was also investigated as low temperature heat source for district heating.…”

Section: Renewable Energy Resources and Technologiesmentioning

confidence: 99%

A review of developments in technologies and research that have had a direct measurable impact on sustainability considering the Paris agreement on climate change

Foley

Smyth

Pukšec

et al. 2017

Renewable and Sustainable Energy Reviews

View full text Add to dashboard Cite

Artificial neural networks for the generation of geothermal maps of ground temperature at various depths by considering land configuration

Cited by 40 publications

References 15 publications

Artificial neural networks for the generation of a conductivity map of the ground

Artificial neural networks for the generation of a conductivity map of the ground

Estimation of renewable energy and built environment-related variables using neural networks – A review

A review of developments in technologies and research that have had a direct measurable impact on sustainability considering the Paris agreement on climate change

Contact Info

Product

Resources

About