Behavior and energy efficiency in long term buildings ...€¦ · •Building energy efficiency...

Behavior and energy efficiency in long term buildings projectionsOreane Y. Edelenbosch, Luciana Miu, Julia Sachs, Massimo Tavoni, Adam

Hawkes

Politecnico di Milano & Imperial College London

1Cobham Buildings Modelling | Edelenbosch | 6-6-2019

Building sector energy transition

• Building energy efficiency plays a important role in long term climate mitigation pathways (IPCC, 2018)

• Two important aspects of buildings energy transition:

1) Technology adoption relies heavily on the residents behavior, depending on attitudes, habits and preferences

2) There is high potential for energy efficiency in improving building shell but inertia associated with new construction and renovation investment.

Cobham Buildings Modelling | Edelenbosch | 6-6-2019 2

Heterogeneous desicionmaking

• Capturing heterogeneity and individual decision making in global, long term models is challenging

• Agent based model could be a appropriate tool

• … but require data!


Agent Based Models:

• Simulating actions and interaction between autonomous agents

• Capture varied drivers of desicion making

• Assess the effect on the system as a whole

Data challenge

• Use empirical data collected during PENNY and Cobham

• Research questions:

1) Can empirical data reveal groups that act in a similar way?

2) Can empirical data to identify drivers of energy-saving investment?

3) How do the empirical grounded model results differ from the conventional macro-economic classification of agent?


Data collection

• PENNY and Cobham data collection

• Cross-country

• Metered electricity consumption

• Energy savings habits

• Energy saving investments

• Socio-demographic characteristics

• Energy usage

• Environmental preferences and attitudes


Data analysis: defining agents

• Energy service indicator was created for lighting and appliances:

• Appliance: Amount and type of appliance

• Lighting: Floorspace and number of bulbs

Energy service indicator: Expected electricity demand for that level of service

Efficiency = Actual electricity consumption – Energy service indicator


Modelling methods

• Cluster analysis based on the energy indicators (lighting service, appliance service, efficiency)

• Clusters examined for patterns in socio-demographics, investment desicions and preferences, habits

• Calibrate MUSE-RSBM to model realistic behavioural heterogeneity in long term energy projections


MUSE-RSBM

Each agent is defined by its own goal, attributes and methodology to solve a specific problem

• Goal: Economical, Environmental, Technology dependent

• Search space: technology visible for agent• Desicion strategy: weighting of different

objectives• Technology maturity treshold

The agents are heterogeneous and have imperfect foresight


Cluster results


Cluster Number of survey responses

Italy Switzerland Netherlands Total

1 643 125 39 807

2 150 147 21 318

3 319 86 40 445

4 1204 70 34 1308

5 1538 116 36 1690

Cluster results: socio-demographics

• Significant difference between clusters

• BUT within cluster variation also clear

• Income has a large affect on service demand


Cluster results: Behaviors and preferences

• Households, who demand and consume energy in very similar ways, cannot easily be mapped to socio-demographic classes, or preferences.


In summary• Cluster 1 is comfortably efficient, living in efficient households, with high appliance service demand than other

clusters. This group are young, live in smaller households and have the lowest residence times. They have good energy-saving habits.

• Cluster 2 is a well-off, medium-efficient group, with varied efficiency, the highest lighting and appliance electricity service. Members have higher education, higher income levels, are older, more energy literate but worse energy-saving habits. They have larger household sizes and value wealth highly.

• Cluster 3 is an inefficient high-consumption group, whose members live exclusively in very inefficient households, consume the most electricity of all. They have the largest household sizes, are fairly well-educated and have high environmental preferences, but have the highest share of never unplugging their appliances to save energy.

• Cluster 4 is an inefficient low-consumption group, whose members live in inefficient households with very low lighting and appliance service demand and medium levels of electricity consumption. They live in the smallest households and dwellings of all groups, are relatively low-educated and have longer residency times than most other groups.

• Cluster 5, the resource-constrained and medium-efficient, is also a low-consuming cluster, where the majority lives in efficient households. They have the lowest income, are slightly less energy literate and live the smallest dwellings of all groups.


Energy efficiency investment

• Energy saving potential and available capital could affect energy investment


Translating the cluster findings to the model


Cluster Characteristics Objectives of agents Constraints Openness to new

technologiesAGENT 1 comfortably efficient Emissions

Efficiency

Potentially constrained by

capital cost

High

AGENT 3 inefficient high-

consumption

Efficiency

Fuel consumption costs

Equivalized annual cost

Not constrained by capital

cost

Low

AGENT 4 inefficient low-

consumption


(partially)

Equivalized annual cost

Capital cost

Potentially constrained by

capital cost

Neutral

AGENT 5 resource-constrained

and medium-efficient

Efficiency


Highly constrained by

capital cost

Neutral

New model results: Heating technologies

• In the long-term technology uptake is comparable

• However diffusion pattern (speed and timing) has changed


New model results: Stochastics

• Accounting for within cluster variation and uncertainty

• Stochasticity around desicion rules and service levels affects 2040-2050 levels


Key findings

• Individual desicion making and interactions, which is difficult to capture by global, long term models, can affect a possible technology transition

• Demonstrate method to develop a empirically grounded Agent Based Model

• The cluster results show that groups that have similar energy profiles cannot easily be mapped to socio-demographic classes

• The new developed empirically grounded model shows that this affects the transition timing and speed – crucial when evaluating energy policies


Thank you for your attention

http://www.cobham-erc-eu


http://www.cobham-erc-eu/

Renovation & new construction

• Technologies are readily available to drastically improve buildings thermal transmittance (Uvalues in W/m²K)

• Investment costs depend on building age

• Difference between new construction and renovation

• Desicions now can carry out for a long period

• So far IAMs do not include a representation of building vintages


U-values improvement possibilities


EU policy:1) Efficient New construction (now)2) Efficient Renovation – within

2050

Drivers of thermal insulation

Building thermal insulation changes with time and climate


Research questions


1) How do building stock and U-values dynamics affect Final Energy Demand for space heating and cooling?

2) What is the current energy gap with the European buildings policy?

3) How much energy can we save by applying European buildings policy globally?

Global Building stock model

• Stock and construction flow dynamics

• Detailed U-value investment choice

• Scenario analysis to test key sensitivities

• Link to global EDGE buildings model Economic, demographic,

climatic, techn.

Building vintages(r,t)

EDGE buildings energy demand

U-Value investment(r,t)


Basic stock model

• Construction(t)=Stock(t)-Stock(t-1)+Demolition

• Stock/cap ~ gdp/cap

• Construction ~ ∆GDP/cap

• Data: European stock data + Global household size


Model 1: Economic drivers of building stock


Model 2: Adjusting for median age


Construction output -- Europe


Vintages output -- Europe


Beyond Europe

• Key difference and uncertainty across regions is building lifetimes affecting demolition rates

• Scarcity of global buildings data

• Comparison of China, Japan, USA, Russia


Renovation decision drivers

For every timestep (for both glazed and opaque surfaces):

• Input data: starting vintage U-values, regional energy efficiencies, prices and energy carrier shares, discount rate, HDD, CDD, technology investment costs

• NPC is computed

Multinomial logit (MNL) equation for technology diffusion:

• 𝑀𝑎𝑟𝑘𝑒𝑡 𝑠ℎ𝑎𝑟𝑒𝑖 =𝑒−λ∗𝑁𝑃𝐶𝑖

σ𝑖=1#𝑡𝑒𝑐𝑛𝑜𝑙𝑜𝑔𝑖𝑒𝑠

𝑒−λ∗𝑁𝑃𝐶𝑖

• Payback time: endogenization of the renovation rate, market shares calculation


Vintage U-value update

When it’s convenient, only a part of the considered vintage is renovated, according to the computed renovation rate

1945-1969U-value: 3 W/m2/K

RenovationU=0.3

W/m2/K1945-1969

With renovation rate r=10%

𝑼𝒗𝒂𝒍𝒖𝒆 = 𝟑 ∗ 𝟏 − 𝒓 + 𝟎. 𝟑 ∗ 𝒓 =

= 𝟐. 𝟕𝟑𝑾

𝒎𝟐𝑲


New construction module - assumptions• Small cost reduction (15%) for opaque components

• Higher payback time is allowed

• The Opaque insulation module is now divided into 2 parts:

1. firstly, the insulation option is computed, with the 3 available technologies; then an aggregated U-value and cost is computed by means of the NPC and MNL

2. The resulting aggregated U-value and cost are compared with a ‘no-insulation’ option, with 0 costs and U-value of concrete material. The NPC of both options is calculated and again, a final U-value is computed by means of the MNL equation, with the climate factor correction


Sensitivity – Energy prices


Sensitivity – Discount rate


Historic regional comparison – before calibration


Comparison with past trends- EU level


Historic regional comparison – after calibration


1) How do building stock and U-values affect Final Energy demand?

38

Europe

Cobham Buildings Modelling | Edelenbosch | 6-6-2019

1) How do building stock and U-values affect Final Energy demand?

39

World


2) What is the current energy gap with the European buildings policy?

40

Europe


3) How much energy can we save by applying European buildings policy globally?

41

World


Key findings

• Energy demand for heating is projected to slightly decrease, cooling will strongly increase

• The energy gap with the European policy is still wide

• 75% energy savings, most of them would be due to new buildings


SSPs assumptions


SSP1 SSP2 SSP3

Lifespan, renovation 40 years 30 years 20 years

Lifespan, new construction

60 years 50 years 40 years

Cost decrease 80% in 2050, 60% in 2100,Strong exponential decrease

80% in 2050, 60% in 2100,Linear decrease

90% in 2050, 75% in 2100,Linear decrease

0

1

2

3

4

5

6

7

8

0 5 10 15 20 25 30 35 40

Ren

ova

tio

n r

ate

[%]

Payback time [years]

SSP1 SSP2 SSP3

Insulation technologies - Opaque parts


Insulation technologies - Windows

Starting U-values and costs (2010)

• Single-glazed: 5.8 W/m2/K, 150 €/m2

• Double-glazed: 3.8 W/m2/K, 220 €/m2

• Double glazed, Thermal break+ low-e coating: 2.3 W/m2/K, 280 €/m2

• Triple-glazed: 1.1 W/m2/K, 400 €/m2

• Future technology: 0.6 W/m2/K, gets into the market after 2050


Payback time calculation𝑁𝑃𝑉 = 𝑆ℎ𝑒𝑎𝑡𝑖𝑛𝑔 + 𝑆𝑐𝑜𝑜𝑙𝑖𝑛𝑔 − 𝐶𝑜𝑠𝑡𝑟𝑒𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛

• 𝐶𝑜𝑠𝑡𝑟𝑒𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛€

𝑚𝑒𝑛𝑣𝑒𝑙𝑜𝑝𝑒2 = 𝐹 + 𝑠 ∗ 𝑎 ∗ 85% +𝑊 ∗ 15%

• 𝑆ℎ𝑒𝑎𝑡𝑖𝑛𝑔€

𝑚𝑒𝑛𝑣𝑒𝑙𝑜𝑝𝑒2 = σ𝑡=1

𝐿𝑖𝑓𝑒𝑠𝑝𝑎𝑛 σ𝑖=1#𝐸𝐶

𝐹𝐸𝐶𝑖 𝑝𝑟𝑖𝑐𝑒€

𝑘𝑊ℎ𝑓𝑖𝑛𝑎𝑙∗𝐸𝐶𝑖%

𝜂𝑈𝐸−𝐹𝐸,𝑖𝑘𝑊ℎ𝑢𝑠𝑒𝑘𝑊ℎ𝑓𝑖𝑛𝑎𝑙

∗ ∆𝐸𝑢𝑠𝑒𝑓𝑢𝑙𝑘𝑊ℎ𝑢𝑠𝑒

𝑚𝑒𝑛𝑣2 ∗ 1 + 𝑟 −𝑡

• ∆𝐸𝑛𝑒𝑟𝑔𝑦𝑢𝑠𝑒𝑓𝑢𝑙,ℎ𝑒𝑎𝑡𝑖𝑛𝑔𝑘𝑊ℎ

𝑚𝑒𝑛𝑣2 ∗𝑦𝑒𝑎𝑟

= ∆𝑈𝑣𝑎𝑙𝑢𝑒𝑊

𝑚2𝐾∗ 𝐻𝐷𝐷 𝐾 ∗ 𝑑𝑎𝑦/𝑦𝑒𝑎𝑟 ∗ 24

ℎ

𝑑/1000

𝑊

𝑘𝑊

• 𝑆𝑐𝑜𝑜𝑙𝑖𝑛𝑔 calculation is the same as 𝑆ℎ𝑒𝑎𝑡𝑖𝑛𝑔


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Behavior and energy efficiency in long term buildings ...€¦ · •Building energy efficiency...

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