Code repository
From HUES Platform Wiki
Revision as of 11:55, 20 December 2016 by Andrewbollinger (Talk | contribs)
Welcome to the code repository. The repository includes two types of code elements, "scripts" and "snippets".
- Scripts are small utility programs for e.g. data processing, transformation or visualization.
- Snippets are small blocks of useful code that you would like to share with others.
Users with a HUES Platform account may also add new scripts or snippets to the repository.
Add a script or snippet to the repository
Snippets are pasted directly into the wiki. Scripts must be uploaded.
List of scripts
| Script name | Author | Tags | Description |
|---|---|---|---|
| Swiss buildings GIS heat demand database | Stefan Schneider (UNIGE) | Geo-dependent Energy Demand | The Swiss buildings GIS heat demand database allows building a database containing an estimation of the yearly final energy demand for space heating and domestic hot water production for each EGID of the Swiss Federal Building Register (Reg-Bl).
The current module provides scripts to create the database and the database tables, to load configuration data and to compute the estimated heat demand per building. The user of this database needs to make a contract with the Federal Office of Statistics (BFS) to get a copy of the Reg-Bl. |
| IDF Viewer | Christoph Waibel | Idf Energyplus Visualization Pareto front | A tool to load in geometry of multiple IDF files and display these in 3D. Additionally, an associated excel file can be loaded, which refers each IDF-file to a point on a 2D-graph. Now, the user is able to hover over the points of the 2D-graph and display only the 3D-geometry of that IDF file, where the mouse points at. This tool is especially helpful in a parametric study, where multiple design solutions have to be compared quickly. E.g. in a multi-objective optimization, the results can be plotted in the 2D-graph to display a 2-objective Pareto-front and the user may now efficiently elaborate the geometrical relationships between the trade-offs of both objectives . |
| Read AIMMS solution header | Ralph Evins | AIMMS Energy hub | Read .sol file using manual referencing within the XML as its faster than a full recursive evaluation If nargout == 1, only reads header (inc objective value), which is much faster for big datasets |
| Parse XML | Ralph Evins | Matlab | Parse an XML file into a Matlab struct |
| Rolling Horizon AIMMS | Julien Marquant | Energy hub model Rolling horizon Computational time Milp | Solving the optimal configuration and operating strategy of an energy hub combining multiple energy sources for a whole year can become computationally demanding. Indeed the effort to solve a mixed-integer linear programming (MILP) problem grows dramatically with the number of integer variables. Rolling Horizon approach (RH) is part of the commonly employed decomposition methods; as the Bender decomposition, Lagrangean decomposition, and Bi-level decomposition; used in order to reduce the computational burden to solve mathematical time-dependent problem with high number of variables. Indeed rather than solving a complex problem considering all its time horizon frame, the problem is solved by planning intervals representing a smaller part of the horizon, allowing to reduce the size of the problem per interval, by breaking down one problem in easily solved sub-problems. From an existing AIMMS model, the implementation of a rolling horizon approach is done in two stages: 1) Systematic creation of a second model using Python script 'AIMMS_Control_Rolling_Horizon.py' (procedure described in documentation, section 2.1): 2) Manual tuning directly done on AIMMS file situated in 'MainProject -> ProjectName.ams' (procedure described in documentation, section 2.2). |
| Edit AIMMS model | Ralph Evins | AIMMS Energy hub | Edit an AIMMS model by manipulating the .ams file |
| Read AIMMS variable names | Ralph Evins | AIMMS Energy hub | Read variable and constraint names from .lis file |
| Read AIMMS data | Ralph Evins | AIMMS Energy hub | |
| Execute AIMMS sequence | Ralph Evins | AIMMS Energy hub | |
| EnergyPlus batch run module | Ralph Evins | Building simulation Energyplus | This module automates the setup and execution of multiple building energy models implemented in EnergyPlus, and consists of a set of Matlab scripts for building, editing and running an EnergyPlus model, and parsing the output. The module works by inserting specified parametric objects into a template EnergyPlus IDF file.
|
| Arc-gis to geo-dependent-energy-web-service | Julien Marquant | WebService | Linking arc-gis database to geo-dependent energy demand web-service. Allowing to extract heating energy demand for each defined rasters in arcgis.
The following script MappingDistrict.cs allows to read your arcgis database in csv format with as arguments the location of the center of your raster in CHLV02 geo referenced system. As output the script writes back a result file with all informations required according to the information available in the WebService database |
| Unsupervised k-medoids typical days | Julien Marquant Georgios Mavromatidis | Typical days | The typical day method used in Dominguez-Munoz et al. (2011) are implemented in a MATLAB code. The k-medoids clustering method is employed selecting k typical days able to represent the full year demand pro file. 3 peaks days for heating, cooling and electricity demand pro files are added. The k number of days is selected by the user. The unsupervised typical days function select the number k of typical days which best represents the full year demand profile. This is done based on a minimization of error between the load duration curves for heating, cooling and electricity and the Davies-Bouldin index representing the intra and inter clusters relation (Davies and Bouldin (1979)). The user select the number k of typical days depending on the pareto front and the computation time. |
