Identifying hybrid heating systems in the residential sector from smart meter data

In this paper, we identify hybrid heating systems on a single residential customer’s premises using smart meter data. A comprehensive methodology is developed at a generic level for residential sector buildings to identify the type of primary and support heating systems. The methodology includes the use of unsupervised and supervised learning algorithms both separately and combined. It is applied to two datasets that vary in size, quality of data, and availability and reliability of background information. The datasets contain hourly electricity consumption profiles of residential customers together with the outdoor temperature. The validation metrics for the developed algorithms are elaborated to provide a probabilistic evaluation of the model. The results show that it is possible to identify the types of both primary and support heating systems in the form of probability of having electric- or non-electric type of heating. The results obtained help estimate the flexibility domain of the residential building sector and thereby generate a high value for the energy system as a whole

Household preferences for energy goods and services:a choice experiment application

Abstract This thesis includes three studies on household preferences for energy goods and services. The first study examines determinants of households’ heating system choices using a choice experiment. The choice sets include six main heating alternatives (district heating, ground heat pump, exhaust air heat pump, solid wood boiler, wood pellet boiler, and electric storage heating) that are described by five attributes (supplementary heating systems, investment costs, operating costs, comfort of use and environmental friendliness). The results imply that hybrid heating appears to be accepted among households. The results also reveal differing preferences for the main heating alternatives and show that they are affected by demographic characteristics. The studied attributes also play a significant role when heating systems are being chosen. The second study is a methodological one that extends the analysis of the first study. The second study explores the effect of perceived choice complexity on the randomness of choices in choice experiments. The study investigates how different self-evaluated factors of choice complexity affect mean scale and scale variance. The findings suggest that perceived choice complexity has a systematic impact on the parameters of econometric models of choice. However, differences exist between alternative self-evaluated complexity-related covariates. The results indicate that individuals who report that answering the choice tasks is more difficult have less deterministic choices. Perceptions of the realism of home heating choice options also affect scale and scale variance. The third study utilizes the choice experiment to analyze households’ willingness to participate in demand side flexibility. The study examines whether individuals are willing to time their electricity usage and heating; whether they are interested in dynamic pricing contracts such as real-time pricing, two-rate tariffs, or power-based tariffs; and how emissions reductions affect their choices. The results indicate that households’ sensitivity to restrictions in electricity usage is much stronger than their sensitivity to restrictions in heating. Households also require compensation to choose real-time pricing over fixed fees. The findings suggest that room may exist for new dynamic electricity distribution contracts, such as power-based tariffs, in the market. Other value-creating elements besides monetary compensation also exist that could incentivize households to offer demand side flexibility because households value power system level reductions in CO2 emissions.Tiivistelmä Tämä väitöskirja koostuu kolmesta tutkimuksesta, joissa tarkastellaan kotitalouksien preferenssejä energiahyödykkeitä ja -palveluita kohtaan. Ensimmäinen tutkimus keskittyy kotitalouksien lämmitysjärjestelmävalintoihin ja niitä määrittäviin tekijöihin. Tämä tutkimus on tehty valintakoemenetelmällä, jonka valintatilanteet sisältävät kuusi eri päälämmitysjärjestelmävaihtoehtoa (kaukolämpö, maalämpöpumppu, puulämmitys, pellettilämmitys, varaava sähkölämmitys ja poistoilmalämpöpumppu). Päälämmitysjärjestelmiä kuvataan viiden ominaisuuden avulla, jotka ovat tukilämmitysjärjestelmä, investointikustannukset, käyttökustannukset, käyttömukavuus ja ympäristöystävällisyys. Tulosten mukaan kotitalouksien preferenssit päälämmitysjärjestelmävaihtoehtoja kohtaan ovat vaihtelevia. Valintaan vaikuttavat sekä tarkastellut ominaisuudet että kotitalouden demografiset tekijät. Tulokset myös paljastavat, että kotitaloudet suhtautuvat myönteisesti hybridilämmitykseen. Toinen tutkimus on menetelmällinen, missä hyödynnetään ensimmäisen tutkimuksen aineistoa. Tämä tutkimus keskittyy yksilöiden kokeman vastaamisen vaikeuden vaikutuksiin valintakoemenetelmässä. Vastaamisen epätarkkuus tunnistetaan valintakoemenetelmässä skaalan ja skaalavarianssin avulla. Tutkimus tarkastelee, kuinka itsearvioidut vastaamisen vaikeutta mittaavat tekijät vaikuttavat keskimääräiseen skaalaan ja skaalavarianssiin valintojen ekonometrisissa malleissa. Tulosten mukaan koettu vastaamisen vaikeus vaikuttaa systemaattisesti ekonometrisen valintamallin parametreihin. Vastaamisen vaikeutta mittaavien tekijöiden välillä on kuitenkin eroja. Tuloksien perusteella vastaajat, jotka kokevat valintatilanteisiin vastaamisen keskimääräistä vaikeampana, tekevät satunnaisempia valintoja. Myös valintatilanteiden koettu realistisuus vaikuttaa skaalaan ja skaalavarianssiin. Kolmannessa tutkimuksessa arvioidaan kotitalouksien halukkuutta osallistua energian kysyntäjoustoon valintakoemenetelmällä. Tämä tutkimus selvittää ovatko kotitaloudet halukkaitta siirtämään sähkönkulutusta ja lämmitystä, ja kuinka kiinnostuneita he ovat dynaamisista sähkön hinnoittelusopimuksista kuten pörssisähkösopimuksesta, yösähkösopimuksesta tai tehoperusteisesta sopimuksesta. Lisäksi tutkitaan vaikuttavatko järjestelmätason päästövähennykset kotitalouksien valintoihin. Tulosten perusteella kotitaloudet suhtautuvat sähkönkulutuksen rajoituksiin selvästi negatiivisemmin kuin lämmityksen rajoituksiin. Kotitaloudet myös vaativat rahallista korvausta valitakseen pörssisähkösopimuksen kiinteähintaisen sopimuksen sijaan. Tulosten mukaan markkinoilla voisi olla tilaa uudenlaisille sopimustyypeille, kuten tehoperusteiselle vaihtoehdolle. Tulokset osoittavat, että kotitaloudet arvostavat järjestelmätason hiilidioksidipäästövähennyksiä. Täten rahallisen korvauksen lisäksi on olemassa myös muita arvoa luovia tekijöitä lisätä kotitalouksien osallistumista kysyntäjoustoon

Towards flexible energy demand:preferences for dynamic contracts, services and emissions reductions

Abstract Households’ preferences for attributes of flexible energy demand are not well understood. This paper evaluates Finnish households’ acceptance of hypothetical contracts and services aimed at increasing demand side flexibility. We conduct a Choice Experiment to analyze households’ willingness to offer flexibility through timing their electricity usage and heating; their interest in dynamic pricing contracts such as real-time pricing, two-rate tariffs, or power-based tariffs; and how emissions reductions affect their choices. The results indicate that households’ sensitivity to restrictions in electricity usage is much stronger than their sensitivity to restrictions in heating. Households also require considerable compensation to choose real-time pricing over fixed fees. Furthermore, other value-creating elements besides monetary compensation could incentivize households to offer demand side flexibility because they value reductions in CO₂ emissions at the power system level

Innovators, followers and laggards in home solar PV:factors driving diffusion in Finland

Abstract Generating electricity from solar energy is a way for households to participate in the ongoing transition to decarbonized and more decentralized energy systems. A large empirical literature has examined the drivers and barriers associated with household solar PV adoption. An emerging strand of this literature investigates what distinguishes earlier adopters from later adopters and non-adopters. However, there is yet limited understanding of the differences between earlier and later adopters, as few papers have applied formal statistical models to compare different customer segments. The present study addresses this gap. We examine how the factors that influence household solar PV choices differ between earlier adopters, potential adopters — households that have considered installing solar PV but have not yet done so — and non-adopters. We analyze these choices using rich data from a household-level survey conducted in Finland. The findings show that the adoption of solar PV is linked to a multitude of socio-demographic and financial factors and personal motivations. There are clear differences in the motives and perceptions of adopters, potential adopters, and non-adopters. Accounting for such differences between customer segments will help to better design and target public policy instruments and marketing campaigns that aim to incentivize and nudge households toward solar PV investments

Identifying hybrid heating systems in the residential sector from smart meter data

Abstract In this paper, we identify hybrid heating systems on a single residential customer’s premises using smart meter data. A comprehensive methodology is developed at a generic level for residential sector buildings to identify the type of primary and support heating systems. The methodology includes the use of unsupervised and supervised learning algorithms both separately and combined. It is applied to two datasets that vary in size, quality of data, and availability and reliability of background information. The datasets contain hourly electricity consumption profiles of residential customers together with the outdoor temperature. The validation metrics for the developed algorithms are elaborated to provide a probabilistic evaluation of the model. The results show that it is possible to identify the types of both primary and support heating systems in the form of probability of having electric- or non-electric type of heating. The results obtained help estimate the flexibility domain of the residential building sector and thereby generate a high value for the energy system as a whole