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.

The xlsx file contains generated building raw data, assignment to groups with the Self-Organizing Map, BMU-s and analysis: rankings, charts, correlations and so on.

Foreground life cycle inventories for three alternative systems of providing heat, cooling, and electricity to a university campus, and other relevant background information for the related study. Data-file 1 provides inventories for three energy supply systems with 2015 average technology, and other background information and calculations for the study. Data-file 2 provides inventories for the three energy supply systems based on 2030 technology. Data-file 3 provides inventories for the two systems affected by allocation choices, with a sensitivity analysis whereby allocation is based on exergy content rather than energy content of products.

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.

This data includes: - models for Athena Impact Estimator for refrigerated .at4 and non-refrigerated warehouses .at4 - conveyor model .spf and output file from SuperPro Designer .xslx - EnergyPlus .idf models, weather files .epw and output results .csv for all refrigerated and non-refrigerated warehouses -Refrigerated and non-refrigerated process-based LCA models .csv for import to SimPro - cut-off link to DataSmart database because this is a commercial database.

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.

Final data set used to calculate willingness to upgrade in response to 6 experimental conditions. Data was collected from a nationally representative sample of U.S. homeowners (based on 2015 census). Data file does not include participants that were removed from the data set based on exclusion criteria outlined in attached manuscript.

The Power_time_series.txt file contains the 34 electrical features related to power that were computed using current and voltage acquisitions made using our own acquisition system based on an Arduino MKR Zero microcontroller with a sampling frequency of 6.25 kHz (see reference "Design of an electricity consumption measurement system for Non Intrusive Load Monitoring", IEEE IREC 2019 from the same authors). In the text file the power time series correspond to the active power P, its harmonic order Pk, where k is ranging between 1 and 15 and the sum of the harmonics PH. There are also the reactive power Q, its harmonic order Qk, where k is ranging between 1 and 15 and the sum of the harmonics QH. All these 34 features are ordered by columns as follows: P, P1, P2, P3, P4, P5, P6, P7, P8, P9, P10, P11, P12, P13, P14, P15, PH, Q, Q1, Q2, Q3, Q4, Q5, Q6, Q7, Q8, Q9, Q10, Q11, Q12, Q13, Q14, Q15, QH These power time series correspond to the same scenario of 12 appliances (microwave, a DVD player, a fan, a screen, a vacuum, a waffle iron, a hair dryer, an iron, a flat iron, a mixer, a CFL and a LED lamp) that are randomly switched on/off every 3 seconds during almost one hour and half. This results in almots 1200 events of diferrent appliances. The text file Events_time_stamping.txt contains the time stamps of each events that were hand-labelled after inspecting the signals. Timestamps of the labeled events were adjusted to match the transitions. The text file Events_labelling corresponds to the labelling of each events (which appliance is responsable of the event). Finally, the file MultivriateAbruptChangeDetectors.pdf is a repport containing the mathematicla derivation of each investigated detector in details.

Codebook and R codes

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