Table of Contents

Introduction

Within the scope of the Hotmaps project, data has been collected at various levels (national, regional and local levels). These data have been generated for four different sectors: residential (single family houses, multifamily houses, and apartment blocks), service (offices, trade, education, health, hotels and restaurants, and other non-residential buildings), industry (iron and steel, non-ferrous metals, paper and printing, non-metallic minerals, chemical industry, food, drink and tobacco, engineering and others not classified),and transport (passenger transport -public, private, rail and freight transport -heavy goods and light commercial vehicles).

All the above-mentioned data sets are stored in Hotmaps data repositories on GitLab and can be accessed and downloaded form there. The Hotmaps data repositories are extensive and composed of more than 70 repositories. In order to provide a better overview of all Hotmaps repositories, here, we clustered them into different classes and provided the direct link to them. For detailed explanations on data collection, methodologies, references, assumptions and limitations of Hotmaps data sets refer to this report [1].

To Top

Building stock

EU building stock

To Top

Heating and cooling demand density map

To Top

Gross floor area density map

To Top

Gross volume density map

To Top

Construction periods

Share of gross floor area in construction periods:

To Top

Population

To Top

Industry

To Top

Climate

To Top

Renewable energy source potential

🔺 The unit of the data set has not been mentioned in the Readme file nor in the datapackage file.

🔺 The unit of the data set has not been mentioned in the Readme file nor in the datapackage file.

To Top

Scenario data

To Top

Technology data

To Top

Profiles

Hourly heat load profiles - Generic profiles

Create your own profile:

🔺 `This information should be provided in the GitLab and load profile CM rather than here.`

Generic files are supposed to enable the user to produce load profiles of his own using own data and a structure year of her/his own choice. For the industrial load profiles, we provided a yearlong profile for the year 2018 (in which the typedays are set in the order of this year). For tertiary and residential load profiles, we provided a yearlong profile for the year 2010. However, we want to give the user the opportunity to use a structure year of his/her choice (Structure year in this context means the order of days in the course of the year).

The profiles provided here are unitless, since they must be scaled during the generation of yearlong profiles.

Please refer to the individual profiles in this wiki or to the respective dataset repositories for more information on the generation of profiles from the generic profiles.

To Top

Residential profiles - Generic

For heating, cooling and hot water, we provided a yearlong profile for the year 2010. However, if users have access to location-specific hourly temperature profiles or to temperature profiles for years other than 2010, we want to give the user the opportunity to use this data in order to generation load profiles with a different structure year or higher precision. Therefore, the generic profiles are supposed to enable the user to produce load profiles of his/her own using own data and a structure year of her/his own choice.

For hot water provision, we assume that demand and thus the corresponding load profile depends on seasonal, weekly and daily influences.

The columns “day type” refers to the type of a day in the week:

  • weekdays = typeday 0;
  • saturday or day before a holiday = typeday 1;
  • sunday or holiday = typeday 2

To integrate a seasonal influence into the demand profile, the column “season” is used.

  • 0 = Summer (15/05 - 14/09)
  • 1 = Winter (1/11 - 20/3)
  • 2 = Transition (21/3 - 14/5 & 15/9 - 31/10)

Yearlong profiles for hot water can be generated from the generic profiles provided here following the following steps:

  1. determining the structure year for which the profiles are generated
  2. ordering the typeday/season tuples according to the selected year
  3. allocating the respective load value for the typeday/season tuple to each hour - scaling the total sum of the annual yearlong profile (i.e. the integral of the profile) according to the annual total demand

For heating and cooling, we assume that demand does not depend on the typeday but only on the hour of the day itself and the outside temperature in the respective hour (for this reason, the columns “type day” and “season” are not relevant for heating and cooling profiles).

Yearlong profiles can be generated from the generic profiles provided in this repository following the following steps:

  1. determining the structure year for which the profiles are generated
  2. choosing the correct combination of hour of the day, temperature and demand from the generic profile for each hour of the year in order to get a yearlong, unitless profile
  3. scaling the total sum of the annual yearlong profile (i.e. the integral of the profile) according to the annual total demand

To Top

Tertiary profiles - Generic

The tertiary sector profile consists of demand from multiple subsectors. The configuration is different for each country. For the respective subsectoral shares per country we refer to the hotmaps WP2 report, section 2.7.3 (https://www.hotmaps-project.eu/wp-content/uploads/2018/03/D2.3-Hotmaps_for-upload_revised-final_.pdf).

For hot water demand we assume that demand is independent from outside temperature, but depends on the type of day in a week and the hour of the day. The column “day type” refers to the type of a day in the week:

  • weekdays = typeday 0;
  • saturday or day before a holiday = typeday 1;
  • sunday or holiday = typeday 2 Hour of the day ranges from 1 (first hour) to 24 (last hour)

Yearlong profiles can be generated from the generic profiles provided here following the following steps: 1. determining the structure year for which the profiles are generated 2. ordering the typedays according to the selected year 3. allocating the respective load value for the type days to each hour 4. scaling the total sum of the annual yearlong profile (i.e. the integral of the profile) according to the annual total demand

For heating and cooling in the tertiary sector, we provided a yearlong profile for the year 2010. However, we want to give the user the opportunity to use a year of his/her choice. Additionally, if users have access to location-specific hourly temperature profiles, we want to give the user the opportunity to use this data in order to generation load profiles with a higher precision. Therefore, the generic profiles are supposed to enable the user to produce load profiles of his/her own using own data and a structure year of her/his own choice.

We assume that demand for heating and cooling in the tertiary sector depends on the typeday, the hour of the day itself and the outside temperature in the respective hour.

The profiles provided here are unitless, since they must be scaled during the generation of yearlong profiles. For the generic profiles for heating and cooling, they are driven by the differences between hours and temperature levels. Additionally, since the tertiary sector is driven by a weekly rhythm, the profiles for heating and cooling in the tertiary sector depend also on the day type. The column “day type” refers to the type of a day in the week:

  • weekdays = typeday 0;
  • saturday or day before a holiday = typeday 1;
  • sunday or holiday = typeday 2

Yearlong profiles can be generated from the generic profiles for tertiary heating and cooling provided in this repository following the following steps:

  1. determining the structure year for which the profiles are generated
  2. choosing the correct combination of day type, hour of the day, temperature and demand from the generic profile for each hour of the year in order to get a yearlong, unitless profile
  3. scaling the total sum of the annual yearlong profile (i.e. the integral of the profile) according to the annual total demand

To Top

Industry profiles - Generic

For the industrial load profiles, we provided a yearlong profile for the year 2018 (in which the typedays are set in the order of this year). However, we want to give the user the opportunity to use a structure year of his/her choice. Structure year in this context means the order of days in the course of the year. The columns “day type” refers to the type of a day in the week:

  • weekdays = typeday 0;
  • saturday or day before a holiday = typeday 1;
  • sunday or holiday = typeday 2

The column “month” refers to the month of the year. 1 = January, 2 = February etc. Yearlong profiles can be generated from the generic profiles provided here following the following steps:

  1. determining the structure year for which the profiles are generated
  2. ordering the typedays for each month according to the selected year
  3. allocating the respective load value for the typeday/month tuple to each hour
  4. scaling the total sum of the annual yearlong profile (i.e. the integral of the profile) according to the annual total demand

To Top

Hourly heat load profiles - Year specific profiles

The year specific (yearlong) profiles provided here are generated on the basis of synthetic hourly profiles for typical days. In this context we emphasize, that profiles are not measured but modelled taking into consideration different factors depending on the profile type:

  • For industrial profiles, amongst others shift work patterns, historical output per month/weekday were considered.
  • For tertiary and residential profiles, profiles are dependent on the type of day in the week (i.e. weekday, Saturday, Sunday/Holiday), the hour of the day and in the case of heating and cooling on outside temperature.

Using the structure of the days in a year, the profiles are assembled to a yearlong demand profile.
All profiles provided here are unitless and normalised to 1 000 000. In order to a profile, it is to be scaled according to the annual demand of the respective region (i.e. so that the profiles integral equals the annual demand per region).

For detailed explanations and a graphical illustration of the dataset please see the Hotmaps WP2 report (section 2.7).

To Top

Residential profiles - Year specific

To Top

Tertiary profiles - Year specific

To Top

Industry profiles - Year specific

To Top

Electricity load profiles

To Top

Temperature profiles

To Top

Transport

To Top

Data sets for the operation of the Hotmaps toolbox

To Top

References

[1] Simon Pezzutto, Stefano Zambotti, Silvia Croce, Pietro Zambelli, Giulia Garegnani, Chiara Scaramuzzino, Ramón Pascual Pascuas, Alyona Zubaryeva, Franziska Haas, Dagmar Exner (EURAC), Andreas Müller (e‐think), Michael Hartner (TUW), Tobias Fleiter, Anna‐Lena Klingler, Matthias Kühnbach, Pia Manz, Simon Marwitz, Matthias Rehfeldt, Jan Steinbach, Eftim Popovski (Fraunhofer ISI) Reviewed by Lukas Kranzl, Sara Fritz (TUW); Online Access

To Top

How to cite

Mostafa Fallahnejad, in Hotmaps-Wiki, Hotmaps-data-repository-structure (May 2019)

To Top

Authors and reviewers

This page is written by Mostafa Fallahnejad*.

  • [x] This page was reviewed by Lukas Kranzl*.

* Energy Economics Group - TU Wien

Institute of Energy Systems and Electrical Drives

Gusshausstrasse 27-29/370

1040 Wien

To Top

License

Copyright © 2016-2019: Mostafa Fallahnejad

Creative Commons Attribution 4.0 International License

This work is licensed under a Creative Commons CC BY 4.0 International License.

SPDX-License-Identifier: CC-BY-4.0

License-Text: https://spdx.org/licenses/CC-BY-4.0.html

To Top

Acknowledgement

We would like to convey our deepest appreciation to the Horizon 2020 Hotmaps Project (Grant Agreement number 723677), which provided the funding to carry out the present investigation.

To Top