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2 results for “power plant database”
JRC Open Power Plants Database (JRC-PPDB-OPEN)
<p>In 2017 the Joint Research Centre developed a Power Plant Database for energy systems modelling (JRC-PPDB) in order to support the unit activities in energy systems modelling and knowledge management.</p> <p>As demand for open data is increasingly sought after, an open version (JRC-PPDB-OPEN), based on exclusively open data was designed. The JRC-PPDB-OPEN is primarily based on a collection of all the information published by ENTSO-E<a href="#_ftn1">[1]</a> on the European power plants at unit level. This information was extended, improved, and where possible corrected using information contained in open datasets published by WRI Powerwatch<a href="#_ftn2">[2]</a>, Global energy observatory<a href="#_ftn3">[3]</a>, FRESNA<a href="#_ftn4">[4]</a> and the EEA<a href="#_ftn5">[5]</a>.</p> <p>The JRC-PPDB-OPEN database is a first attempt towards a more detailed and coherent, albeit still incomplete, dataset of European power plants. Further work to expand and improve the information contained therein is called for. To this extend, and in order to facilitate the future involvement of third parties in such efforts, the associations between records in the different datasets (linkage) are included.</p> <p> </p> <p><a href="#_ftnref1">[1]</a> <a href="https://transparency.entsoe.eu/">https://transparency.entsoe.eu/</a></p> <p><a href="#_ftnref2">[2]</a> <a href="http://datasets.wri.org/dataset/globalpowerplantdatabase">http://datasets.wri.org/dataset/globalpowerplantdatabase</a></p> <p><a href="#_ftnref3">[3]</a> <a href="http://globalenergyobservatory.org/">http://globalenergyobservatory.org/</a></p> <p><a href="#_ftnref4">[4]</a> <a href="https://github.com/FRESNA/powerplantmatching">https://github.com/FRESNA/powerplantmatching</a></p> <p><a href="#_ftnref5">[5]</a> <a href="https://prtr.eea.europa.eu/#/home">https://prtr.eea.europa.eu/#/home</a></p>
Database of thermal power plant of YASSA-DIBAMBA CAMEROUN (2017-2018-2019-2020-2021)
<p>In this work, genetic algorithm coupled with neural networks was used to predict the concentration of pollutants in the Yassa area. The model takes into account the meteorological parameters of the study area and the source over 5 years, period from January 2017 to December 2021. To evaluate the model, two indices are used to indicate the prediction model’s performance; the Squared Correlation Coefficient R<sup>2</sup> whose value in the test case is 0.9996 and the Mean Square Error (MSE) whose best value is 0.0044756. We also evaluated the optimization methods and found that compared to the swarm by particle optimization, the genetic algorithm gives a better “fitness function curve” as a function of the number of iterations. The results showed that the GA-ANN coupling presented here is able to provide a reliable and accurate prediction of the distance of the area where the air quality standards and criteria are met. The safe distance where thermal power plant activity respects the air quality criteria is beyond 2200m. The results help not only in the choice of the location of the thermal power plant but also in the monitoring of the air quality in residential areas.</p>
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