Energy & Buildings

This dataset, related to the eponymous paper in journal Energy and Buildings, contains data of 6 air handling units with manually indiced faults. The paper is needed, it containts deiails about the fault induction and more detailed specifications of the AHU. Dataset contains classified "non-fault data" as well as "faulty data", in order to allow validation of fault detection and diagnostic tools.

Data for the two case studies.

Supplementary Building Energy Templates Include: Student Housing Building Classrooms with Labs Classrooms with Auditoriums Classrooms with Dancehalls Code for Energy model and optimization The Python notebook file may be used to model different buildings similar to the (4) template files. Building size can be adjusted and a local weather file can be input to model different sized buildings in different locations. The Multi-Objective Optimization code can be used to define the Pareto Front using the results from the building energy model. Instructions (Python File): Download the anaconda notebook Download the desired weather file an save it in the 'Documents' folder Download the template files and save it in 'Documents' folder Download the cooling tower Neural Network "Cooling_Tower_NN.h5" and sae it in the 'Documents' folder Run the code. Be sure to have all helper libraries installed

The attached files are the base case with different simulation data.

This supplementary data presents the search results and the summary of the features selected to analyze for a systematic review of metamodels based on artificial neural networks for building performance simulation. The files information is described below: * Search_1.pdf: Search results for "metamodel + building + energy" on Google Scholar. Date of last search: November 15th, 2019. * Search_2.pdf: Search results for "surrogate model + building + energy" on Google Scholar. Date of last search: November 15th, 2019. * SelectedSearchResults.xlsx: Filtered literature results with several summary tables showing the features analized.

Variance Inflation factors for the OLS regression as presented in Table 5 and stepwise OLS regressions for October to April.

Data in summary: 1- Building total B side: This is metered data from one of two mains busbars that supplies all none-emergency services and HVAC equipment 2- Building total A side: This is metered data from the second of two mains busbars that supplies all emergency services including fire safety, comm rooms, emergency lighting and public announcement. It also is connected to a PV array with peak electrical supply of around 33kWe. 3- Half hourly building demand and deferrable load breakdowns: This is processed data that includes building total and HH instances of deferrable loads for all sub-categories of loads considered in this work. It also includes HH instances of PV generation, and outside air temperature. 4- Early morning ramp rates following plant start-up: This is a file containing the difference between two instantaneous recordings of total building electricity consumption that shows the continuous fluctuation in total electricity demand in the building. 5- CO2-raw (Typical office): This files contains actual CO2 data in an office that represents typical space occupant density in the case study building. 6- CO2-raw (worst case): This files contains actual CO2 data in a teaching space that represents the highest observed space occupant density in the case study building. 7- Warming and cooling rates in the worst case zones: This file include actual data describing the operational temperature in the worst case zones most prone to overheating in summer and excessive heat loss in winter.

Converted Weather Data in EPW format for Toronto Pearson Airport from CWEEDs weather data in WYEC3 format.

The following zip file contains the codes and research data for the implementation of the simulation optimization framework proposed in the associated paper. Detailed step-by-step instructions for running the code is provided in the "Instructions.docx" file. You will need both MATLAB and GAMS (with an MIP solver) installed on your computer to be able to use the code and replicate the results presented in the paper.

Future design weather data (epw. files) for evaluating the building thermal-energy performance in Hong Kong using the downscaled data from 24 general circulation models (GCMs) in the CMIP5. It includes six sets of future design weather data under three time slices (2035s, 2065s, 2090s) of two climate change scenarios (RCP4.5 and RCP8.5) for Hong Kong.