Title: 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"


Citation
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. http://doi.org/10.17035/d.2018.0063429446



Access Rights: Data can be made freely available subject to attribution

Access Method: Click to email a request for this data to opendata@cardiff.ac.uk


Dataset Details

Publisher: Cardiff University

Date (year) of data becoming publicly available: 2018

Data format: .idf, .xlsx

Software Required: 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.

Estimated total storage size of dataset: Less than 100 megabytes

Number of Files In Dataset: 7

DOI : 10.17035/d.2018.0063429446

DOI URL: http://doi.org/10.17035/d.2018.0063429446


Description

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


Keywords

Energy minimization, machine learning, metaheuristic algorithms

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Last updated on 2021-24-03 at 12:12