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


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, .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

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



Allweddeiriau

Energy minimization, machine learning, metaheuristic algorithms

Prosiectau Cysylltiedig

Diweddarwyd y tro diwethaf ar 2019-21-10 am 09:46