- Email: [email protected]
In search for market-based energy efficiency investment in households: smart home solutions as an option for optimized use of energy and reduction of costs for energy
Available online at www.sciencedirect.com Available online at www.sciencedirect.com
ScienceDirect ScienceDirect
Availableonline onlineatatwww.sciencedirect.com www.sciencedirect.com Available Energy Procedia 00 (2018) 000–000
Energy Procedia 00 (2018) 000–000 ScienceDirect ScienceDirect
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
EnergyProcedia Procedia00147 (2018) 1–6 Energy (2017) 000–000 www.elsevier.com/locate/procedia
International Scientific Conference “Environmental and Climate Technologies”, CONECT 2018 International Scientific Conference “Environmental and Climate Technologies”, CONECT 2018
In search for market-based energy efficiency investment in The for 15th market-based International Symposium on District Heating investment and Cooling in In search energy efficiency households: smart home solutions as an option for optimized use of households: smart solutions as anthe option demand-outdoor for optimized use of Assessing thehome feasibility of using energy and reduction of costsheat for energy of costs forheat energy temperature energy functionand forreduction a long-term district demand forecast Reinis Aboltins*, Dagnija Blumberga Reinis Aboltins*, Dagnija Blumberga a a b c I. Andrić *, Systems A. Pina , P. Ferrão J. Fournier B. Lacarrière , O. LeLatvia Correc Institute of Energy and Environment, Riga,Technical University,.,Azenes iela 12/1, Riga, LV-1048, a,b,c
Institute of Energy Systems and Environment, Riga Technical University, Azenes iela 12/1, Riga, LV-1048, Latvia IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal b Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France Abstract a
Abstract Investment in energy efficiency measures lacks regulatory incentives, and a lot depends on how actively energy efficiency is promoted byinutilities. large-scale energy lacks efficiency does not work, consumers to getonmotivated individually. Utilities are Investment energy If efficiency measures regulatory incentives, and a lothave depends how actively energy efficiency is Abstract well positioned to deploy market-based energy efficiency initiatives thatconsumers require accessible investment from householdsUtilities and energy promoted by utilities. If large-scale energy efficiency does not work, have to get motivated individually. are efficiency product from utilities. Smart energy home solutions includethat energy efficiency elements are being on theand market by well positioned to deploy market-based efficiencythat initiatives require accessible investment fromtested households energy District heating networks are commonly addressed in the literature as one of the most effective solutions for decreasing the utility companies search of a mutual – energy that and include cost saving for the consumer and profit for utilities. efficiency productinfrom utilities. Smart benefit home solutions energy efficiency elements are being tested on the market by greenhouse gas emissions from the building sector. These systems require high investments which are returned through the heat Mass deployment ofsearch technologies benefiting in terms of energy depends on applicability utility companies in of a mutual benefitconsumers – energy and cost saving for efficiency the consumer and profit for utilities.and acceptance of sales. Due to the changed climate conditions and building renovation policies, heat demand in the future could decrease, technologies and gains for the consumers – economy, comfort and security. from the Mass deployment of technologies benefitingexpressed consumersininvalue terms propositions of energy efficiency depends on applicability andData acceptance of prolonging the investment return period. households involved in testing home solutions installation–ofeconomy, smart home technologies has hadData no immediate technologies and gains for thesmart consumers expressedindicates in valuethat propositions comfort and security. from the The main scope of this paper is to assess the feasibility of using the heat demand – outdoor temperature function for heat demand impact on energy consumption and costs forsolutions energy. Some of the affecting the results identified, has but had further households involved in testing smart home indicates thatfactors installation of smart homeare technologies no qualitative immediate forecast. The district of Alvalade, located in Lisbon (Portugal), was used as a case study. The district is consisted of 665 analysison is energy neededconsumption for a more detailed insight that would allow drawing conclusions about whatare has to and canbut befurther done toqualitative stimulate impact and costs for energy. Some of the factors affecting the results identified, buildings that vary in both construction period and typology. Three weather scenarios (low, medium, high) and three district investment in innovative energy efficiency solutions. Indicatively, the answersabout rest what within analysis is needed for a more detailed insight that would allow drawing conclusions hasconsumer to and canbehavior, be done toregulatory stimulate renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were environment, relatedenergy to the energy market, and ability and willingness to invest. investment infactors innovative efficiency solutions. Indicatively, the answers rest within consumer behavior, regulatory compared with results from a dynamic heat demand model, previously developed and validated by the authors. environment, factors related to the energy market, and ability and willingness to invest. The results showed that when only weather change is considered, the margin of error could be acceptable for some applications © 2018 The Authors. Published by Elsevier Ltd. (the error in annual Published demand was lower than 20% for all weather scenarios considered). However, after introducing renovation © 2018 The Authors. by Ltd. This is an open accessPublished article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) © 2018 The Authors. by Elsevier Elsevier Ltd. scenarios, the access error value to BY-NC-ND 59.5% (depending the weather and renovation scenarios combination considered). This is an open articleincreased under theupCC license on (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and access peer-review responsibility of the scientific committee of the International Scientific Conference ) This is an open article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/ The value slope coefficient increased onofaverage withincommittee the rangeofofthe3.8% up to 8% per decade, that corresponds to the Selection andofpeer-review under responsibility the scientific International Scientific Conference ‘Environmental ‘Environmental and ClimateCONECT Technologies’, CONECT Selection and peer-review under responsibility of 2018. the scientific committee of the International Scientific Conference and Climate Technologies’, 2018. decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and ‘Environmental and Climate Technologies’, CONECT renovation scenarios considered). On the other hand,2018. function intercept increased for 7.8-12.7% per decade (depending on the Keywords: smart home solutions; energy efficiency; households; investments; energy costs coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and Keywords: smart home solutions; energy efficiency; households; investments; energy costs
improve the accuracy of heat demand estimations.
© 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and * Corresponding author. Cooling.
E-mail address:author. [email protected] * Corresponding E-mail address: [email protected] Keywords: Heat demand; Forecast; Climate change 1876-6102 © 2018 The Authors. Published by Elsevier Ltd. This is an open access under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) 1876-6102 © 2018 Thearticle Authors. Published by Elsevier Ltd. Selection under responsibility of the scientific of the International Scientific Conference ‘Environmental and Climate This is an and openpeer-review access article under the CC BY-NC-ND licensecommittee (https://creativecommons.org/licenses/by-nc-nd/4.0/) Technologies’, CONECT 2018. Selection and peer-review under responsibility of the scientific committee of the International Scientific Conference ‘Environmental and Climate 1876-6102 © 2017 The Authors. Technologies’, CONECT 2018. Published by Elsevier Ltd. 1876-6102 2018 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of the scientific committee of the International Scientific Conference ‘Environmental and Climate Technologies’, CONECT 2018. 10.1016/j.egypro.2018.07.026
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Reinis Aboltins et al. / Energy Procedia 147 (2018) 1–6 Author name / Energy Procedia 00 (2018) 000–000
1. Introduction The main goal of the current research is to assess, in the context of energy efficiency measures for households and based on information and data from actual test objects, if market-based energy efficiency solutions influence actual energy consumption and cost of energy in households to an extent that allows promoting energy efficiency related smart home solutions savings being the key value proposition. Utility company LTC in Latvia in telecom and energy services is expanding its services to include smart home solutions that are intended to primarily (but not exclusively) address energy saving and reduction of costs for energy in households as well as SME segment. A study on households was initiated to test the impact of smart home services on energy consumption and energy costs in households. A prior market research by the utility company shows that target market in Latvia for investment in innovative energy efficiency technologies is relatively small (circa 50,000) and is primarily represented by so called early adopters of new technologies and technological solutions [1]. To be able to reach the target group most effectively test households have been selected that ensure direct feedback from users of actual energy efficiency related smart home solutions (service prototypes). The approach is to create and develop a common platform that allows integration of devices of third-party producers and combining the devices to make a variety of energy efficiency and home automation solutions possible. To achieve mass deployment the solutions have to be plug-and-play or near-plug-and-play. Scalability of smart home solutions requires that installation of most common solutions have to be non-invasive vis-a-vis the existing infrastructure and in terms of ability of consumers to buy, install and configure the devices themselves. Ability and willingness to make an initial investment is essential for the launch of innovative energy efficiency services. It is important to note that market-based deployment of energy efficiency solutions has the potential to contribute significantly to overall reduction in energy consumption, especially during hours of peak energy demand [2], through a combination of smart metering, smart energy management and smart distribution and transmission networks. Acquisition of quantitative and qualitative data is part of the exercise to collect, aggregate and analyze factors affecting energy use patterns in households, which choose to invest in smart home solutions [1–3]. Feedback from the test group as well as results from a pool of 50 qualitative interviews indicates that households are hesitant to invest in innovative energy efficiency (based largely on management of heating system) solutions unless the service provider can explain all the gains in sufficient detail and provide a feasible calculation about energy saving and reduction of expenditure for energy. It is interesting to note that energy saving and reduction of costs of energy are mentioned as key factors affecting decision-making about investment in smart home energy efficiency solutions, but feedback on energy consumption and habits from test households shows that after a period of testing devices that ensure energy saving, actual energy consumption tends to remain the same or there is even an increase in energy consumption, and comfort-related considerations in favor of smart home solutions prevail over energy saving considerations. Investment in energy efficiency is largely related to the cost of energy efficiency, as consumers want to know what is going to be the period of return on investment (ROI) in energy efficiency measures. Ability to allocate share of monthly income for energy efficiency purposes is limited, quoting in the study the cost of investment among key factors why households have not invested in innovative energy efficiency solutions. One of the main questions is what is in place for the consumer to be motivated to invest in energy efficiency and change energy consumer behaviour. Legislative environment is not too favourable, introduction of smart energy metering technologies does not provide incentive for consumers to track their energy consumption, welfare aspects also play a role. Given a comparatively low energy consumption per capita, households tend to acquire household appliances that they have not had previously, thus energy efficiency does not necessarily mean reduction in overall energy consumption [1, 4]. The hypothesis of the ongoing study by the utility company is that a smart home solution associated with efficient use of energy allows saving up to 40 % of energy costs compared with the costs in the same object prior to the installation of energy-efficiency related smart home solution. To test the hypothesis decision was made by the utility company (LTC) to carry out tests from among a large pool of employees (circa 2,000) of LTC. Over 20 criteria were used to select nine objects. Owners of the potential test objects had to demonstrate behaviour of an early adopter of technologies and willing to experiment with new solutions that would be tested prior and in parallel to developing a prototype service and putting the product on the market for mass deployment.
Reinis Aboltins et al. / Energy Procedia 147 (2018) 1–6 Author name / Energy Procedia 00 (2018) 000–000
3 3
2. Methodology Five private houses and four apartments were selected as test objects. As there was a need to test impact of energy efficiency solutions in different circumstances, nine objects were deliberately chosen each having different parameters, existing household equipment, different heating systems, energy consumption patterns.
Smart home concept
Decision about investments
Selection of pilot cases
Methodology for data analysis with selection of indicators
Recomendations for next development activities
Analysis and comparison of results
Data collection and analysis after first investments
Data collection and analysis before investments
Fig. 1. Step by step approach.
One of the main challenges – how to make data from different objects mutually comparable, as the goal of installing devices in the test objects is to test empirically whether the energy efficiency solution installed in a particular object has caused any changes in energy consumption in the same particular object. Different combination of devices has been installed in nine different objects, there are no two similar objects. Thus each of the test objects has to be initially studied separately by collecting quantitative and qualitative data over a longer period of time preferably exceeding 12 months or at least two full heating seasons. Information and data from the most recent heating season has been collected and used to begin analysis of impact on energy consumption and energy costs of using smart home solutions. Only limited data from the study is used to discuss a number of immediate observations. More data and analysis will be used as the study develops, as a more comprehensive set of conclusions is needed to also justify continuation of investment in energy efficiency related smart home services. Analysis is also expected to provide suggestions for communication with target groups of smart home services as well as policy-makers on the changes required in legislation to facilitate investment in energy efficiency. Energy prices do influence households, but to a lesser extent than could be presumed. This can be explained by stability of energy consumption habits, relatively cheap energy, and low energy consumption per capita compared to the same indicator in economically more developed countries – energy consumption tends to grow with the growth of welfare and stops or starts decreasing once a certain threshold of welfare or economic stability is reached (in a household) [1, 5, 6]. Market conditions can also play an important role in energy consumption habits. Latvia experienced a downward trend of electricity prices over last two years, which did not facilitate thinking about saving in terms of reducing energy consumption. Thus, as it has been noted in other sources [2], investment in innovative energy efficiency did not necessarily result in energy saving, and consumption stayed the same or increased as one could consume more energy for the same costs. Energy consumer behaviour is a complex realm to analyse [5], but a general assumption is that the best motivator for behavioural changes in favour of saving energy is high energy prices or deteriorating welfare conditions [7]. Involvement of consumers in mass deployment of energy efficiency requires policy measures, subsidies or, in general, incentives that allow consumers seeing gains from investing in energy efficiency. 3. First results To establish whether the installed solution (set of devices) has an effect on the consumption of primary energy or/and costs associated with consumption of primary energy, heat and electricity, it is necessary to analyse the relevant data. The data is submitted by the residents of the respective test objects. Historical data before the installation of
Reinis Aboltins et al. / Energy Procedia 147 (2018) 1–6 Author name / Energy Procedia 00 (2018) 000–000
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devices and the most recent data after the installation of the devices has been collected. Data from October through March in 2016–2017 and 2017–2018 is compared: this is typical six months long heating season in Latvia, when energy consumption is usually at its high. The data includes: Electricity consumption; Consumption of primary energy (objects with gas or pellet boiler for water heating and heating for radiators and water heated floors); Cost of energy (electricity and heating); Heated space area; Number of inhabitants in the household. The four objects represented have following characteristics: Object represented in Table 1 is a private house with 219 m2 area, 2 inhabitants, gas boiler; Object represented in Table 2 is an apartment with 75 m2 area, 2 inhabitants, district heating; Object represented in Table 3 is an apartment with 72 m2 area, 2 inhabitants in season 2016–2017 and 3 inhabitants in season 2017–2018, gas boiler; Object represented in Table 4 is a private house with 200 m2 area, 4 inhabitants in season 2016–2017 and 5 inhabitants in season 2017–2018, pellet boiler. Quantitative analysis of initial data from the test objects does not allow making conclusions about the motivation of consumers to invest in smart home solutions associated with energy efficiency. The study shows that energy consumption in general has increased (Table 1, Table 2, Table 3 and Table 4) and that there has been only one instance of decrease of monthly power consumption per area unit among the four test objects featured in the present version of the study (Table 2). Table 1. Data and analysis for pilot case 1. Electricity consumption, kWh Area, m
Persons
2
Season
House/LSv
Oct
Nov
Dec
Jan
Feb
Mar
1
2
3
4
5
6
Total
kWh/m2
kWh/pp
219
2
2016–2017
181
181
238
186
172
162
1120
0.85
560
219
2
2017–2018
359
447
446
472
687
837
3248
2.47
1624
178
266
208
286
515
675
2128
1.62
1064
2017–2018 vs 2016–2017 Table 2. Data and analysis for pilot case 2.
Electricity consumption, kWh Area, m
2
Persons
Season
Apt/AR
Oct
Nov
Dec
Jan
Feb
Mar
1
2
3
4
5
6
Total
kWh/m2
kWh/pp
75
2
2016–2017
145
175
140
150
145
135
890
1.98
445
75
2
2017–2018
125
185
200
180
190
160
1040
2.31
520
–20
10
60
30
45
25
150
0.33
75
2017–2018 vs 2016–2017
Qualitative information is key to understanding the particular quantitative trends. Situation in a household may have changed, which explains increase of power consumption per unit of area. In the particular study, power consumption per capita has decreased slightly in those households where the number of persons living in the household
Reinis Aboltins et al. / Energy Procedia 147 (2018) 1–6 Author name / Energy Procedia 00 (2018) 000–000
5 5
increased. Presence of an extra person in the household triggered an increase in energy consumption, but also decreased energy consumption per person (kWh/pp) in the household (Table 3 and Table 4). Table 3. Data and analysis for pilot case 3. Electricity consumption, kWh Area, m
Persons
2
Season
Apt/LSp
Oct
Nov
Dec
Jan
Feb
Mar
1
2
3
4
5
6
Total
kWh/m2
kWh/pp
72
2
2016–2017
68
75
94
78
88
93
496
1.15
248
72
3
2017–2018
93
75
117
108
111
114
618
1.43
206
25
0
23
30
23
21
122
0.28
–42
Total
kWh/m2
kWh/pp
1856
1.55
464
2017–2018 vs 2016–2017 Table 4. Data and analysis for pilot case 4.
Electricity consumption, kWh Area, m
Persons
2
Season
House/VK 200
4
2016–2017
200
5
2017–2018
2017–2018 vs 2016–2017
Oct
Nov
Dec
Jan
Feb
Mar
1
2
3
4
5
6
322
287
375
316
280
276
394
335
410
388
334
389
2250
1.88
450
72
48
35
72
54
113
394
0.33
–14
It has to be noted that the study also intends to address monitoring of detailed (as opposite to overall) energy consumption in the test households with the purpose of identifying power consumption by source. Power monitoring devices have been installed in the four test objects referred to in the current research, however, shortcomings in the functioning of the devices does not allow drawing any conclusions bar those related to metering and API as well as physical installation and ease of use issues. 2,500 Normalised monthly energy consumption, kWh/m2 month
2,000 2016-17 1,500 1,000
-6
-4
-2
0,000
Linear (2016-17) Linear (2017-18)
y = –0.0739x + 1.1603 R2 = 0.736
0,500
-8
2017-18
y = –0.1026x + 1.447 R2 = 0.9276
0
2
4
6
8
Outdoor temperature, °C Fig. 2. Normalised monthly electricity consumption versus outdoor temperature.
The data in Fig. 2 reflects situation in one object (pilot case 3 represented in Table 3) with the total heated area of 72 m2. Data has been normalised by using temperature adjustment index (calculated using 0 °C as a reference standard) to make data comparison on specific energy consumption possible.
Reinis Aboltins et al. / Energy Procedia 147 (2018) 1–6 Author name / Energy Procedia 00 (2018) 000–000
6 6
Fig. 2 indicates that data on 2016–2017 correlates well (R2 = 0.74) with the empirical equation of specific energy consumption:
E 1.16 – 0.074 t outd , kWh/m2 month
(1)
Data scattering in this case could be explained by consumer behaviour showing lack of interest in saving energy. Fig. 2 illustrates that data from the 2017–2018 heating season correlates (R2 = 0.92) with the empirical equation of specific energy consumption better than during 2016–2017 heating season:
E 1.45 – 0.103 t outd , kWh/m2 month
(2)
During the 2017–2018 heating season, specific energy consumption increased showing higher increase during colder period, while significantly decreasing during warmer months. This can be explained by consumer behaviour and changes in the particular household: total consumption of energy increased as the number of persons living in the household increased by one – an infant required higher comfort temperature for longer periods of time, plus members of the household spent more time indoors, compared with the previous heating season. Behaviour changes present better correlation of data and difference of slope of curve in last period. 4. Conclusions
Interesting unpredicted data was observed during the first period of testing smart home solutions: increase of electricity consumption shows changes in behaviour of energy end users, which can possibly be explained by complexity of smart home solutions. In this case increase of comfort conditions plays significant role. Although energy saving is a dominant factor in favour of choosing smart home solution, comfort tends to replace savings as the main value proposition. This pilot study serves as an initial marker for further deliberation about how to achieve market-based mass deployment of smart technological solutions that are presumed to carry the value of saving energy and reducing cost of energy for the end-consumer. Based on the agreement with energy end users of the test objects the study will continue for at least one more heating season to accumulate more data and make it possible to see what changes in energy consumption can be attributed to what factors – technology, climate, energy prices (primary and transformed), number of inhabitants in a test household, consumer behaviour, or other. Further analysis will allow adapting business strategy for more effective deployment of market-based energy efficiency solutions associated with smart home technologies. References [1] [2] [3] [4] [5] [6] [7]
Heiskanen E, Matschoss K. Understanding the uneven diffusion of building-scale renewable energy systems: A review of household, local and country level factors in diverse European countries. Renewable and Sustainable Energy Reviews 2017;75:580–91. Foulds C, Robison RAV, Macrorie R. Energy monitoring as a practice: Investigating use of the iMeasure online energy feedback tool. Energy Policy 2017;104:194–202. Morgan T. The techno-finance fix: A critical analysis of international and regional environmental policy documents and their implications for planning. Progress in Planning 2018;119:1–29. Girod B, Stucki T, Woerter M. How do policies for efficient energy use in the household sector induce energy-efficiency innovation? An evaluation of European countries. Energy Policy 2017;103:223–37. Belaid F. Untangling the complexity of the direct and indirect determinants of the residential energy consumption in France: Quantitative analysis using a structural equation modelling approach. Energy Policy 2017;110:246–56. Belaid F, Bakaloglou S, Roubaud D. Direct rebound effect of residential gas demand: Empirical evidence from France. Energy Policy 2018;115:23–31. Bye B, Faehn T, Rosnes O. Residential energy efficiency policies: Costs, emissions and rebound effects. Energy 2018;143:191–201.
ScienceDirect ScienceDirect
Availableonline onlineatatwww.sciencedirect.com www.sciencedirect.com Available Energy Procedia 00 (2018) 000–000
Energy Procedia 00 (2018) 000–000 ScienceDirect ScienceDirect
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
EnergyProcedia Procedia00147 (2018) 1–6 Energy (2017) 000–000 www.elsevier.com/locate/procedia
International Scientific Conference “Environmental and Climate Technologies”, CONECT 2018 International Scientific Conference “Environmental and Climate Technologies”, CONECT 2018
In search for market-based energy efficiency investment in The for 15th market-based International Symposium on District Heating investment and Cooling in In search energy efficiency households: smart home solutions as an option for optimized use of households: smart solutions as anthe option demand-outdoor for optimized use of Assessing thehome feasibility of using energy and reduction of costsheat for energy of costs forheat energy temperature energy functionand forreduction a long-term district demand forecast Reinis Aboltins*, Dagnija Blumberga Reinis Aboltins*, Dagnija Blumberga a a b c I. Andrić *, Systems A. Pina , P. Ferrão J. Fournier B. Lacarrière , O. LeLatvia Correc Institute of Energy and Environment, Riga,Technical University,.,Azenes iela 12/1, Riga, LV-1048, a,b,c
Institute of Energy Systems and Environment, Riga Technical University, Azenes iela 12/1, Riga, LV-1048, Latvia IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal b Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France Abstract a
Abstract Investment in energy efficiency measures lacks regulatory incentives, and a lot depends on how actively energy efficiency is promoted byinutilities. large-scale energy lacks efficiency does not work, consumers to getonmotivated individually. Utilities are Investment energy If efficiency measures regulatory incentives, and a lothave depends how actively energy efficiency is Abstract well positioned to deploy market-based energy efficiency initiatives thatconsumers require accessible investment from householdsUtilities and energy promoted by utilities. If large-scale energy efficiency does not work, have to get motivated individually. are efficiency product from utilities. Smart energy home solutions includethat energy efficiency elements are being on theand market by well positioned to deploy market-based efficiencythat initiatives require accessible investment fromtested households energy District heating networks are commonly addressed in the literature as one of the most effective solutions for decreasing the utility companies search of a mutual – energy that and include cost saving for the consumer and profit for utilities. efficiency productinfrom utilities. Smart benefit home solutions energy efficiency elements are being tested on the market by greenhouse gas emissions from the building sector. These systems require high investments which are returned through the heat Mass deployment ofsearch technologies benefiting in terms of energy depends on applicability utility companies in of a mutual benefitconsumers – energy and cost saving for efficiency the consumer and profit for utilities.and acceptance of sales. Due to the changed climate conditions and building renovation policies, heat demand in the future could decrease, technologies and gains for the consumers – economy, comfort and security. from the Mass deployment of technologies benefitingexpressed consumersininvalue terms propositions of energy efficiency depends on applicability andData acceptance of prolonging the investment return period. households involved in testing home solutions installation–ofeconomy, smart home technologies has hadData no immediate technologies and gains for thesmart consumers expressedindicates in valuethat propositions comfort and security. from the The main scope of this paper is to assess the feasibility of using the heat demand – outdoor temperature function for heat demand impact on energy consumption and costs forsolutions energy. Some of the affecting the results identified, has but had further households involved in testing smart home indicates thatfactors installation of smart homeare technologies no qualitative immediate forecast. The district of Alvalade, located in Lisbon (Portugal), was used as a case study. The district is consisted of 665 analysison is energy neededconsumption for a more detailed insight that would allow drawing conclusions about whatare has to and canbut befurther done toqualitative stimulate impact and costs for energy. Some of the factors affecting the results identified, buildings that vary in both construction period and typology. Three weather scenarios (low, medium, high) and three district investment in innovative energy efficiency solutions. Indicatively, the answersabout rest what within analysis is needed for a more detailed insight that would allow drawing conclusions hasconsumer to and canbehavior, be done toregulatory stimulate renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were environment, relatedenergy to the energy market, and ability and willingness to invest. investment infactors innovative efficiency solutions. Indicatively, the answers rest within consumer behavior, regulatory compared with results from a dynamic heat demand model, previously developed and validated by the authors. environment, factors related to the energy market, and ability and willingness to invest. The results showed that when only weather change is considered, the margin of error could be acceptable for some applications © 2018 The Authors. Published by Elsevier Ltd. (the error in annual Published demand was lower than 20% for all weather scenarios considered). However, after introducing renovation © 2018 The Authors. by Ltd. This is an open accessPublished article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) © 2018 The Authors. by Elsevier Elsevier Ltd. scenarios, the access error value to BY-NC-ND 59.5% (depending the weather and renovation scenarios combination considered). This is an open articleincreased under theupCC license on (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and access peer-review responsibility of the scientific committee of the International Scientific Conference ) This is an open article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/ The value slope coefficient increased onofaverage withincommittee the rangeofofthe3.8% up to 8% per decade, that corresponds to the Selection andofpeer-review under responsibility the scientific International Scientific Conference ‘Environmental ‘Environmental and ClimateCONECT Technologies’, CONECT Selection and peer-review under responsibility of 2018. the scientific committee of the International Scientific Conference and Climate Technologies’, 2018. decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and ‘Environmental and Climate Technologies’, CONECT renovation scenarios considered). On the other hand,2018. function intercept increased for 7.8-12.7% per decade (depending on the Keywords: smart home solutions; energy efficiency; households; investments; energy costs coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and Keywords: smart home solutions; energy efficiency; households; investments; energy costs
improve the accuracy of heat demand estimations.
© 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and * Corresponding author. Cooling.
E-mail address:author. [email protected] * Corresponding E-mail address: [email protected] Keywords: Heat demand; Forecast; Climate change 1876-6102 © 2018 The Authors. Published by Elsevier Ltd. This is an open access under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) 1876-6102 © 2018 Thearticle Authors. Published by Elsevier Ltd. Selection under responsibility of the scientific of the International Scientific Conference ‘Environmental and Climate This is an and openpeer-review access article under the CC BY-NC-ND licensecommittee (https://creativecommons.org/licenses/by-nc-nd/4.0/) Technologies’, CONECT 2018. Selection and peer-review under responsibility of the scientific committee of the International Scientific Conference ‘Environmental and Climate 1876-6102 © 2017 The Authors. Technologies’, CONECT 2018. Published by Elsevier Ltd. 1876-6102 2018 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of the scientific committee of the International Scientific Conference ‘Environmental and Climate Technologies’, CONECT 2018. 10.1016/j.egypro.2018.07.026
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Reinis Aboltins et al. / Energy Procedia 147 (2018) 1–6 Author name / Energy Procedia 00 (2018) 000–000
1. Introduction The main goal of the current research is to assess, in the context of energy efficiency measures for households and based on information and data from actual test objects, if market-based energy efficiency solutions influence actual energy consumption and cost of energy in households to an extent that allows promoting energy efficiency related smart home solutions savings being the key value proposition. Utility company LTC in Latvia in telecom and energy services is expanding its services to include smart home solutions that are intended to primarily (but not exclusively) address energy saving and reduction of costs for energy in households as well as SME segment. A study on households was initiated to test the impact of smart home services on energy consumption and energy costs in households. A prior market research by the utility company shows that target market in Latvia for investment in innovative energy efficiency technologies is relatively small (circa 50,000) and is primarily represented by so called early adopters of new technologies and technological solutions [1]. To be able to reach the target group most effectively test households have been selected that ensure direct feedback from users of actual energy efficiency related smart home solutions (service prototypes). The approach is to create and develop a common platform that allows integration of devices of third-party producers and combining the devices to make a variety of energy efficiency and home automation solutions possible. To achieve mass deployment the solutions have to be plug-and-play or near-plug-and-play. Scalability of smart home solutions requires that installation of most common solutions have to be non-invasive vis-a-vis the existing infrastructure and in terms of ability of consumers to buy, install and configure the devices themselves. Ability and willingness to make an initial investment is essential for the launch of innovative energy efficiency services. It is important to note that market-based deployment of energy efficiency solutions has the potential to contribute significantly to overall reduction in energy consumption, especially during hours of peak energy demand [2], through a combination of smart metering, smart energy management and smart distribution and transmission networks. Acquisition of quantitative and qualitative data is part of the exercise to collect, aggregate and analyze factors affecting energy use patterns in households, which choose to invest in smart home solutions [1–3]. Feedback from the test group as well as results from a pool of 50 qualitative interviews indicates that households are hesitant to invest in innovative energy efficiency (based largely on management of heating system) solutions unless the service provider can explain all the gains in sufficient detail and provide a feasible calculation about energy saving and reduction of expenditure for energy. It is interesting to note that energy saving and reduction of costs of energy are mentioned as key factors affecting decision-making about investment in smart home energy efficiency solutions, but feedback on energy consumption and habits from test households shows that after a period of testing devices that ensure energy saving, actual energy consumption tends to remain the same or there is even an increase in energy consumption, and comfort-related considerations in favor of smart home solutions prevail over energy saving considerations. Investment in energy efficiency is largely related to the cost of energy efficiency, as consumers want to know what is going to be the period of return on investment (ROI) in energy efficiency measures. Ability to allocate share of monthly income for energy efficiency purposes is limited, quoting in the study the cost of investment among key factors why households have not invested in innovative energy efficiency solutions. One of the main questions is what is in place for the consumer to be motivated to invest in energy efficiency and change energy consumer behaviour. Legislative environment is not too favourable, introduction of smart energy metering technologies does not provide incentive for consumers to track their energy consumption, welfare aspects also play a role. Given a comparatively low energy consumption per capita, households tend to acquire household appliances that they have not had previously, thus energy efficiency does not necessarily mean reduction in overall energy consumption [1, 4]. The hypothesis of the ongoing study by the utility company is that a smart home solution associated with efficient use of energy allows saving up to 40 % of energy costs compared with the costs in the same object prior to the installation of energy-efficiency related smart home solution. To test the hypothesis decision was made by the utility company (LTC) to carry out tests from among a large pool of employees (circa 2,000) of LTC. Over 20 criteria were used to select nine objects. Owners of the potential test objects had to demonstrate behaviour of an early adopter of technologies and willing to experiment with new solutions that would be tested prior and in parallel to developing a prototype service and putting the product on the market for mass deployment.
Reinis Aboltins et al. / Energy Procedia 147 (2018) 1–6 Author name / Energy Procedia 00 (2018) 000–000
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2. Methodology Five private houses and four apartments were selected as test objects. As there was a need to test impact of energy efficiency solutions in different circumstances, nine objects were deliberately chosen each having different parameters, existing household equipment, different heating systems, energy consumption patterns.
Smart home concept
Decision about investments
Selection of pilot cases
Methodology for data analysis with selection of indicators
Recomendations for next development activities
Analysis and comparison of results
Data collection and analysis after first investments
Data collection and analysis before investments
Fig. 1. Step by step approach.
One of the main challenges – how to make data from different objects mutually comparable, as the goal of installing devices in the test objects is to test empirically whether the energy efficiency solution installed in a particular object has caused any changes in energy consumption in the same particular object. Different combination of devices has been installed in nine different objects, there are no two similar objects. Thus each of the test objects has to be initially studied separately by collecting quantitative and qualitative data over a longer period of time preferably exceeding 12 months or at least two full heating seasons. Information and data from the most recent heating season has been collected and used to begin analysis of impact on energy consumption and energy costs of using smart home solutions. Only limited data from the study is used to discuss a number of immediate observations. More data and analysis will be used as the study develops, as a more comprehensive set of conclusions is needed to also justify continuation of investment in energy efficiency related smart home services. Analysis is also expected to provide suggestions for communication with target groups of smart home services as well as policy-makers on the changes required in legislation to facilitate investment in energy efficiency. Energy prices do influence households, but to a lesser extent than could be presumed. This can be explained by stability of energy consumption habits, relatively cheap energy, and low energy consumption per capita compared to the same indicator in economically more developed countries – energy consumption tends to grow with the growth of welfare and stops or starts decreasing once a certain threshold of welfare or economic stability is reached (in a household) [1, 5, 6]. Market conditions can also play an important role in energy consumption habits. Latvia experienced a downward trend of electricity prices over last two years, which did not facilitate thinking about saving in terms of reducing energy consumption. Thus, as it has been noted in other sources [2], investment in innovative energy efficiency did not necessarily result in energy saving, and consumption stayed the same or increased as one could consume more energy for the same costs. Energy consumer behaviour is a complex realm to analyse [5], but a general assumption is that the best motivator for behavioural changes in favour of saving energy is high energy prices or deteriorating welfare conditions [7]. Involvement of consumers in mass deployment of energy efficiency requires policy measures, subsidies or, in general, incentives that allow consumers seeing gains from investing in energy efficiency. 3. First results To establish whether the installed solution (set of devices) has an effect on the consumption of primary energy or/and costs associated with consumption of primary energy, heat and electricity, it is necessary to analyse the relevant data. The data is submitted by the residents of the respective test objects. Historical data before the installation of
Reinis Aboltins et al. / Energy Procedia 147 (2018) 1–6 Author name / Energy Procedia 00 (2018) 000–000
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devices and the most recent data after the installation of the devices has been collected. Data from October through March in 2016–2017 and 2017–2018 is compared: this is typical six months long heating season in Latvia, when energy consumption is usually at its high. The data includes: Electricity consumption; Consumption of primary energy (objects with gas or pellet boiler for water heating and heating for radiators and water heated floors); Cost of energy (electricity and heating); Heated space area; Number of inhabitants in the household. The four objects represented have following characteristics: Object represented in Table 1 is a private house with 219 m2 area, 2 inhabitants, gas boiler; Object represented in Table 2 is an apartment with 75 m2 area, 2 inhabitants, district heating; Object represented in Table 3 is an apartment with 72 m2 area, 2 inhabitants in season 2016–2017 and 3 inhabitants in season 2017–2018, gas boiler; Object represented in Table 4 is a private house with 200 m2 area, 4 inhabitants in season 2016–2017 and 5 inhabitants in season 2017–2018, pellet boiler. Quantitative analysis of initial data from the test objects does not allow making conclusions about the motivation of consumers to invest in smart home solutions associated with energy efficiency. The study shows that energy consumption in general has increased (Table 1, Table 2, Table 3 and Table 4) and that there has been only one instance of decrease of monthly power consumption per area unit among the four test objects featured in the present version of the study (Table 2). Table 1. Data and analysis for pilot case 1. Electricity consumption, kWh Area, m
Persons
2
Season
House/LSv
Oct
Nov
Dec
Jan
Feb
Mar
1
2
3
4
5
6
Total
kWh/m2
kWh/pp
219
2
2016–2017
181
181
238
186
172
162
1120
0.85
560
219
2
2017–2018
359
447
446
472
687
837
3248
2.47
1624
178
266
208
286
515
675
2128
1.62
1064
2017–2018 vs 2016–2017 Table 2. Data and analysis for pilot case 2.
Electricity consumption, kWh Area, m
2
Persons
Season
Apt/AR
Oct
Nov
Dec
Jan
Feb
Mar
1
2
3
4
5
6
Total
kWh/m2
kWh/pp
75
2
2016–2017
145
175
140
150
145
135
890
1.98
445
75
2
2017–2018
125
185
200
180
190
160
1040
2.31
520
–20
10
60
30
45
25
150
0.33
75
2017–2018 vs 2016–2017
Qualitative information is key to understanding the particular quantitative trends. Situation in a household may have changed, which explains increase of power consumption per unit of area. In the particular study, power consumption per capita has decreased slightly in those households where the number of persons living in the household
Reinis Aboltins et al. / Energy Procedia 147 (2018) 1–6 Author name / Energy Procedia 00 (2018) 000–000
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increased. Presence of an extra person in the household triggered an increase in energy consumption, but also decreased energy consumption per person (kWh/pp) in the household (Table 3 and Table 4). Table 3. Data and analysis for pilot case 3. Electricity consumption, kWh Area, m
Persons
2
Season
Apt/LSp
Oct
Nov
Dec
Jan
Feb
Mar
1
2
3
4
5
6
Total
kWh/m2
kWh/pp
72
2
2016–2017
68
75
94
78
88
93
496
1.15
248
72
3
2017–2018
93
75
117
108
111
114
618
1.43
206
25
0
23
30
23
21
122
0.28
–42
Total
kWh/m2
kWh/pp
1856
1.55
464
2017–2018 vs 2016–2017 Table 4. Data and analysis for pilot case 4.
Electricity consumption, kWh Area, m
Persons
2
Season
House/VK 200
4
2016–2017
200
5
2017–2018
2017–2018 vs 2016–2017
Oct
Nov
Dec
Jan
Feb
Mar
1
2
3
4
5
6
322
287
375
316
280
276
394
335
410
388
334
389
2250
1.88
450
72
48
35
72
54
113
394
0.33
–14
It has to be noted that the study also intends to address monitoring of detailed (as opposite to overall) energy consumption in the test households with the purpose of identifying power consumption by source. Power monitoring devices have been installed in the four test objects referred to in the current research, however, shortcomings in the functioning of the devices does not allow drawing any conclusions bar those related to metering and API as well as physical installation and ease of use issues. 2,500 Normalised monthly energy consumption, kWh/m2 month
2,000 2016-17 1,500 1,000
-6
-4
-2
0,000
Linear (2016-17) Linear (2017-18)
y = –0.0739x + 1.1603 R2 = 0.736
0,500
-8
2017-18
y = –0.1026x + 1.447 R2 = 0.9276
0
2
4
6
8
Outdoor temperature, °C Fig. 2. Normalised monthly electricity consumption versus outdoor temperature.
The data in Fig. 2 reflects situation in one object (pilot case 3 represented in Table 3) with the total heated area of 72 m2. Data has been normalised by using temperature adjustment index (calculated using 0 °C as a reference standard) to make data comparison on specific energy consumption possible.
Reinis Aboltins et al. / Energy Procedia 147 (2018) 1–6 Author name / Energy Procedia 00 (2018) 000–000
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Fig. 2 indicates that data on 2016–2017 correlates well (R2 = 0.74) with the empirical equation of specific energy consumption:
E 1.16 – 0.074 t outd , kWh/m2 month
(1)
Data scattering in this case could be explained by consumer behaviour showing lack of interest in saving energy. Fig. 2 illustrates that data from the 2017–2018 heating season correlates (R2 = 0.92) with the empirical equation of specific energy consumption better than during 2016–2017 heating season:
E 1.45 – 0.103 t outd , kWh/m2 month
(2)
During the 2017–2018 heating season, specific energy consumption increased showing higher increase during colder period, while significantly decreasing during warmer months. This can be explained by consumer behaviour and changes in the particular household: total consumption of energy increased as the number of persons living in the household increased by one – an infant required higher comfort temperature for longer periods of time, plus members of the household spent more time indoors, compared with the previous heating season. Behaviour changes present better correlation of data and difference of slope of curve in last period. 4. Conclusions
Interesting unpredicted data was observed during the first period of testing smart home solutions: increase of electricity consumption shows changes in behaviour of energy end users, which can possibly be explained by complexity of smart home solutions. In this case increase of comfort conditions plays significant role. Although energy saving is a dominant factor in favour of choosing smart home solution, comfort tends to replace savings as the main value proposition. This pilot study serves as an initial marker for further deliberation about how to achieve market-based mass deployment of smart technological solutions that are presumed to carry the value of saving energy and reducing cost of energy for the end-consumer. Based on the agreement with energy end users of the test objects the study will continue for at least one more heating season to accumulate more data and make it possible to see what changes in energy consumption can be attributed to what factors – technology, climate, energy prices (primary and transformed), number of inhabitants in a test household, consumer behaviour, or other. Further analysis will allow adapting business strategy for more effective deployment of market-based energy efficiency solutions associated with smart home technologies. References [1] [2] [3] [4] [5] [6] [7]
Heiskanen E, Matschoss K. Understanding the uneven diffusion of building-scale renewable energy systems: A review of household, local and country level factors in diverse European countries. Renewable and Sustainable Energy Reviews 2017;75:580–91. Foulds C, Robison RAV, Macrorie R. Energy monitoring as a practice: Investigating use of the iMeasure online energy feedback tool. Energy Policy 2017;104:194–202. Morgan T. The techno-finance fix: A critical analysis of international and regional environmental policy documents and their implications for planning. Progress in Planning 2018;119:1–29. Girod B, Stucki T, Woerter M. How do policies for efficient energy use in the household sector induce energy-efficiency innovation? An evaluation of European countries. Energy Policy 2017;103:223–37. Belaid F. Untangling the complexity of the direct and indirect determinants of the residential energy consumption in France: Quantitative analysis using a structural equation modelling approach. Energy Policy 2017;110:246–56. Belaid F, Bakaloglou S, Roubaud D. Direct rebound effect of residential gas demand: Empirical evidence from France. Energy Policy 2018;115:23–31. Bye B, Faehn T, Rosnes O. Residential energy efficiency policies: Costs, emissions and rebound effects. Energy 2018;143:191–201.