Title: 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"

Citation
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


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, .csv
Software Required: 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.
Estimated total storage size of dataset: Less than 100 megabytes
Number of Files In Dataset: 30
DOI: 10.17035/d.2018.0047045867

Description

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



Keywords

Energy minimization, machine learning, metaheuristic algorithms

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Last updated on 2020-03-08 at 13:11