HeatMap Visualizing Waste of Heating Energy

Semanco Workshop Barcelona/Spain, 2013-4-11/12 Mathias Fraaß, Berlin

Beuth Hochschule Berlin

Hochschule für Technik und Wirtschaft, Berlin

Royal Institute of Technology Stockholm

Technical Research Center, Helsinki

Deos Rheine/Berlin

Inhouse Engineering Berlin

THEMATIC LINE 1: URBAN ENERGY DATA

▪ Sourcing energy data from consumers

▪ Energy performance indicators

THEMATIC LINE 2: INTERACTIVE INTERFACES

▪ Visualizing energy data

▪ Developing tools and platforms

THEMATIC LINE 3: BUSINESS MODELS

▪ Strategies for CO2 reduction

▪ Business models

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Mathias Fraaß, Berlin Semanco Workshop Barcelona/Spain, 2013-4-11/12 2

Energy savings in bad insulated buildings

Distribution of Site Energy EU 2010 (BDH)

Buildings41%

Industrial Sector28%

Transportations31%

ENERGY CONSUMPTION IN BUILDINGS

15 % electric, 85 % thermal energy

Germany: ≈ 30% of PE consumption in room heating

DISTRIBUTION OF BUILDINGS (GERMANY)

≈ 20 % with good, 80 % with bad insulation standard

SAVINGS IN BAD INSULATED BUILDINGS

≈ 20 % average saving by optimization (Optimus 2004)

> 30 % savings by performance contractings

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Mathias Fraaß, Berlin Semanco Workshop Barcelona/Spain, 2013-4-11/12 3

Theoretical savings after optimization

OPTMIZATION MEASUREMENTS

▪ Adjustment of hydraulic balance including radiators

▪ Adjustment of supply temperature characteristic

THEORETICAL SAVINGS

(red. Qg + red. Qs + red. Qd + red. Qce) / QP

< 5% in bad insulated buildings (high Qh)

PrimaryEnergy

QPHeating Energy QH (Site Energy) Heating Energy Demand Qh

Temperature ti0

Change of air β0

Qg Qs Qd Qce(generation) (storage) (distribution) (control and emission)

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Mathias Fraaß, Berlin Semanco Workshop Barcelona/Spain, 2013-4-11/12 4

Practical savings after optimization

REAL LIVE SITUATION

▪ Overheated rooms due to inappropriate set points

▪ Additional losses due to window opening

PRACTICAL SAVINGS

red. ΔQP / (QP0 + ΔQP )

20..35 % in public buildings

Heizenergie QH (Endenergie)

Qg0 + ΔQg

Primär-energie

Qp

β0 + Δβ ti0 + Δti

Qs0 + ΔQs Qd0 + ΔQd Qce0 + ΔQce

Qh0QP0

ΔQP

QH0

ΔQHΔQh

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Mathias Fraaß, Berlin Semanco Workshop Barcelona/Spain, 2013-4-11/12 5

Improving the distribution of energy waste

OPTIMIZATION / CENTRAL AUTOMATION

▪ Lower waste potential supplied

▪ Lower effective waste

USER PARTICIPATION / DECENTRAL AUTOMATION

▪ Higher frequency of lower waste rates

▪ Lower effective waste

wϕwϕs

h(wϕ)

wϕm wϕm

wϕwϕswϕswϕm wϕm

h(wϕ)

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Mathias Fraaß, Berlin Semanco Workshop Barcelona/Spain, 2013-4-11/12 6

STUDY AREA

▪ Two single storeys in Beuth HS and HTW

OBJECTIVES

▪ Determining waste profiles and the improvements reached by user participation and optimization

▪ Finding the cheapest and most feasable way of establishing building heatmaps

▪ Establishing the heatmap as part of an energy management system according to ISO 50001

PROSPECTIVES

▪ Developing a business model based on implementing and maintaining building heatmaps

▪ Establishing regional and transregional heatmaps from data of building heatmaps

▪ Establishing waste variables such as the waste potential as KPI´s in guidelines

Heatmap