Teitl: Raw data supporting the results presented in the article "A Zone-Level, Building Energy Optimisation Combining an Artificial Neural Network, a Genetic Algorithm, and Model Predictive Control"

Dyfyniad
Reynolds J, Rezgui Y, Kwan ASK, et al. (2018). Raw data supporting the results presented in the article "A Zone-Level, Building Energy Optimisation Combining an Artificial Neural Network, a Genetic Algorithm, and Model Predictive Control". Cardiff University. https://doi.org/10.17035/d.2018.0047045867

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, .csv

Meddalwedd ofynnol: EnergyPlus V8.3 - 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: 30

DOI : 10.17035/d.2018.0047045867

DOI URL: http://doi.org/10.17035/d.2018.0047045867

Related URL: https://doi.org/10.1016/j.energy.2018.03.113

Disgrifiad

The dataset includes an EnergyPlus energy model of a small office building based in Cardiff, UK. From this energy model, several simulations were run with varying heating set point temperatures to produce a training dataset for an artificial neural network. The training dataset is comprised of weather, occupancy, time and date information to predict the indoor temperature of all 6 occupied zones as well as the heating energy consumption of each.

The dataset also contains the results of an optimisation strategy designed to minimise the energy consumption of the building. This shows the baseline energy consumption, energy consumption due to day ahead optimisation, energy consumption due to model predictive control. This is broken down for 5 test days, each zone, and under a standard and time of use tariff.

Research results based upon these data are published at https://doi.org/10.1016/j.energy.2018.03.113