Teitl: Raw data supporting the results presented in the article "Operational Supply and Demand Optimisation of a Multi-Vector District Energy System using Artificial Neural Networks and a Genetic Algorithm"
Dyfyniad
Reynolds J, Ahmad M, Rezgui Y, et al. (2018). Raw data supporting the results presented in the article "Operational Supply and Demand Optimisation of a Multi-Vector District Energy System using Artificial Neural Networks and a Genetic Algorithm". Cardiff University. https://doi.org/10.17035/d.2018.0063429446
Hawliau Mynediad: Gall data fod ar gael yn rhad ac am ddim yn amodol ar briodoli
Dull Mynediad: I anfon cais i gael y data hwn, ebostiwch opendata@caerdydd.ac.uk
Crewyr y Set Ddata o Brifysgol Caerdydd
Manylion y Set Ddata
Cyhoeddwr: Cardiff University
Dyddiad (y flwyddyn) pryd y daeth y data ar gael i'r cyhoedd: 2018
Fformat y data: .idf, .xlsx
Meddalwedd ofynnol: EnergyPlus V8.4 - Open Access software publicly available at https://energyplus.net/ is required to run the model. Any text editor can be used to simply view the model.
Amcangyfrif o gyfanswm maint storio'r set ddata: Llai na 100 megabeit
Nifer y ffeiliau yn y set ddata: 7
DOI : 10.17035/d.2018.0063429446
DOI URL: http://doi.org/10.17035/d.2018.0063429446
The dataset includes the components that made up the virtual eco-district that was optimised in this study. This includes the EnergyPlus building simulation models of the School, Apartment Block, Hospital, Hotel, and Office buildings. In addition, the recorded solar photovotaic generation data from St Teilo's school in Cardiff in included. A complete record of the optimisation results is provided. This spreadsheet contains the optimised set points of each controllable energy generation source (CHP, heat pump, gas boiler and thermal storage), the set point temperature, average indoor temperature and energy consumption of the controllable office building, and the corresponding data for the baseline scenario against which the optimisation was compared. Research results based upon these data are published at https://doi.org/10.1016/j.apenergy.2018.11.001
Disgrifiad
Allweddeiriau
Energy minimization, machine learning, metaheuristic algorithms
Prosiectau Cysylltiedig
- Real-time and semantic energy management across buildings in a district configuration (01.10.2015 - 31.03.2019)