Heat Load Profiles in Industry and the Tertiary Sector: Correlation with Electricity Consumption and Ex Post Modeling (Supplemental Data)
| dc.contributor.author | {"last":"Jesper","first":"Mateo","affiliation":"University of Kassel - Department of Solar and Systems Engineering","id":"orcid","id_value":"0000-0001-7475-2377"} | |
| dc.contributor.author | {"last":"Pag","first":"Felix","affiliation":"University of Kassel - Department of Solar and Systems Engineering"} | |
| dc.contributor.author | {"last":"Vajen","first":"Klaus","affiliation":"University of Kassel - Department of Solar and Systems Engineering"} | |
| dc.contributor.author | {"last":"Jordan","first":"Ulrike","affiliation":"University of Kassel - Department of Solar and Systems Engineering"} | |
| dc.date.accessioned | 2022-03-28T12:25:00Z | |
| dc.date.available | 2022-03-28T12:25:00Z | |
| dc.identifier.uri | https://daks.uni-kassel.de/handle/123456789/43 | |
| dc.identifier.uri | http://dx.doi.org/10.48662/daks-9 | |
| dc.description | This is the dataset relative to the research article "Heat Load Profiles in Industry and the Tertiary Sector: Correlation with Electricity Consumption and Ex Post Modeling". It contains two types of graphs for 82 industrial companies and large consumers from tertiary sector for the years 2018 and 2019:<br><br> Type 1 - Original Load Profiles and Correlation: (a): Normalized heat load profile with a daily resolution. The daily heat consumption is normalized to the mean heat consumption on working days with a mean ambient temperature of 8 °C. (b): Normalized electricity consumption with a daily resolution. The daily electricity consumption is normalized to the maximum daily heat consumption. (c): Normalized daily heat consumption versus normalized daily electricity consumption. <br><br> Type 2 - Prediction: (a): Real load profile and prediction of the ex ante 3 model . The ex ante 3 model is based on a linear regression. (b): Ex ante 3 prediction of daily heat consumption versus real daily heat consumption. (c): Real load profile and prediction of the ex post 1 model . The ex post 1 model combines a linear regression and a shallow learning (NuSVR) approach. (d): Ex post 1 prediction of daily heat consumption versus real daily heat consumption. (e): Real load profile and prediction of the ex post 2 model . The ex post 2 model is deep learning (LSTM) model. (f): Ex post 2 prediction of daily heat consumption versus real daily heat consumption. | en |
| dc.description.sponsorship | {"funderName":"German Federal Ministry for Economic Affairs and Climate Action","awardNumber":"03ETW014A"} | |
| dc.language.iso | eng | de_DE |
| dc.relation | {"relationType":"IsSupplementTo","relatedIdentifierType":"DOI","id_value":"https://doi.org/10.3390/su14074033"} | |
| dc.rights | Creative Commons Attribution Share-Alike 4.0 | |
| dc.rights.uri | https://creativecommons.org/licenses/by-sa/4.0/ | |
| dc.subject | Heat Load Profiles | de_DE |
| dc.subject | Electricity Load Profiles | de_DE |
| dc.subject | Prediction | de_DE |
| dc.subject | Machine Learning | de_DE |
| dc.subject.classification | 404-01 Energieverfahrenstechnik | de_DE |
| dc.subject.ddc | 620 | |
| dc.title | Heat Load Profiles in Industry and the Tertiary Sector: Correlation with Electricity Consumption and Ex Post Modeling (Supplemental Data) | de_DE |
| dc.type | Image | de_DE |
| local.ka.faculty | FB15:Maschinenbau | de_DE |
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