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Day-Ahead Predictions of Electricity Consumption in a Swedish Office Building from Weather, Occupancy, and Temporal data
KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems. (Power System Management with related Information Exchange (PSMIX))ORCID iD: 0000-0002-9860-4472
Uppsala Universitet.
KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems. (Power System Management with related Information Exchange (PSMIX))ORCID iD: 0000-0003-3014-5609
Department of Energy Technology, SP Technical Research Institute of Sweden.
2015 (English)In: Energy and Buildings, ISSN 0378-7788, E-ISSN 1872-6178, Vol. 108Article in journal (Refereed) Published
Abstract [en]

An important aspect of Demand Response (DR) is to make accurate predictions for the consumption in the short term, in order to have a benchmark load profile which can be compared with the load profile influenced by DR signals. In this paper, a data analysis approach to predict electricity consumption on load level in office buildings on a day-ahead basis is presented. The methodology is: (i) exploratory data analysis, (ii) produce linear models between the predictors (weather and occupancies) and the outcomes (appliance, ventilation, and cooling loads) in a step wise function, and (iii) use the models from (ii) to predict the consumption levels with day-ahead prognosis data on the predictors. The data has been collected from a Swedish office building floor. The results from (ii) show that occupancy is correlated with appliance load, and outdoor temperature and a temporal variable defining work hours are connected with ventilation and cooling load. It is concluded from the results in (iii) that the error rate decreases if fewer predictors are included in the predictions. This is because of the inherent forecast errors in the day-ahead prognosis data. The achieved error rates are comparable with similar prediction studies in related work.

Place, publisher, year, edition, pages
Elsevier, 2015. Vol. 108
Keyword [en]
Office Building Electricity Consumption, Load Level, Building Energy Management System, HVAC, Exploratory Data Analysis, Prediction, Regression, Demand Response.
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
URN: urn:nbn:se:kth:diva-172802DOI: 10.1016/j.enbuild.2015.08.052ISI: 000365364500028ScopusID: 2-s2.0-84943265845OAI: diva2:849613

QC 20160114

Available from: 2015-08-29 Created: 2015-08-29 Last updated: 2016-03-29Bibliographically approved
In thesis
1. Modeling and Simulation of Electricity Consumption Profiles in the Northern European Building Stock
Open this publication in new window or tab >>Modeling and Simulation of Electricity Consumption Profiles in the Northern European Building Stock
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The electric power systems are currently being transformed through the integration of intermittent renewable energy resources and new types of electric loads. These developments run the risk of increasing mismatches between electricity supply and demand, and may cause non-favorable utilization rates of some power system components. Using Demand Response (DR) from flexible loads in the building stock is a promising solution to overcome these challenges for electricity market actors. However, as DR is not used at a large scale today, there are validity concerns regarding its cost-benefit and reliability when compared to traditional investment options in the power sector, e.g. network refurbishment. To analyze the potential in DR solutions, bottom-up simulation models which capture consumption processes in buildings is an alternative. These models must be simple enough to allow aggregations of buildings to be instantiated and at the same time intricate enough to include variations in individual behaviors of end-users. This is done so the electricity market actor can analyze how large volumes of flexibility acts in various market and power system operation contexts, but also can appreciate how individual end-users are affected by DR actions in terms of cost and comfort.

The contribution of this thesis is bottom-up simulation models for generating load profiles in detached houses and office buildings. The models connect end-user behavior with the usage of appliances and hot water loads through non-homogenous Markov chains, along with physical modeling of the indoor environment and consumption of heating and cooling loads through lumped capacitance models. The modeling is based on a simplified approach where openly available data and statistics are used, i.e. data that is subject to privacy limitations, such as smart meter measurements are excluded. The models have been validated using real load data from detached houses and office buildings, related models in literature, along with energy-use statistics from national databases. The validation shows that the modeling approach is sound and can provide reasonably accurate load profiles as the error results are in alignment with related models from other research groups.

This thesis is a composite thesis of five papers. Paper 1 presents a bottom-up simulation model to generate load profiles from space heating, hot water and appliances in detached houses. Paper 2 presents a data analytic framework for analyzing electricity-use from heating ventilation and air conditioning (HVAC) loads and appliance loads in an office building. Paper 3 presents a non-homogeneous Markov chain model to simulate representative occupancy profiles in single office rooms. Paper 4 utilizes the results in paper 2 and 3 to describe a bottom-up simulation model that generates load profiles in office buildings including HVAC loads and appliances. Paper 5 uses the model in paper 1 to analyze the technical feasibility of using DR to solve congestion problems in a distribution grid.

Abstract [sv]

Integrering av förnybara energikällor och nya typer av laster i de elektriska energisystemen är möjliga svar till klimatförändringar och uttömning av ändliga naturresurser. Denna integration kan dock öka obalanserna mellan utbud och efterfrågan av elektricitet, och orsaka en ogynnsam utnyttjandegrad av vissa kraftsystemkomponenter. Att använda efterfrågeflexibilitet (Demand Response) i byggnadsbeståndet är en möjlig lösning till dessa problem för olika elmarknadsaktörer. Men eftersom efterfrågeflexibilitet inte används i stor skala idag finns det obesvarade frågor gällande lösningens kostnadsnytta och tillförlitlighet jämfört med traditionella investeringsalternativ i kraftsektorn. För att analysera efterfrågeflexibilitetslösningar är botten-upp-simuleringsmodeller som fångar elförbrukningsprocesser i byggnaderna ett alternativ. Dessa modeller måste vara enkla nog för att kunna representera aggregeringar av många byggnader men samtidigt tillräckligt komplicerade för att kunna inkludera unika slutanvändarbeteenden. Detta är nödvändigt när elmarknadsaktören vill analysera hur stora volymer efterfrågeflexibilitet påverkar elmarknaden och kraftsystemen, men samtidigt förstå hur styrningen inverkar på den enskilda slutanvändaren. 

Bidraget från denna avhandling är botten-upp-simuleringsmodeller för generering av elförbrukningsprofiler i småhus och kontorsbyggnader. Modellerna kopplar slutanvändarbeteende med elförbrukning från apparater och varmvattenanvändning tillsammans med fysikaliska modeller av värmedynamiken i byggnaderna. Modellerna är byggda på en förenklad approach som använder öppen data och statistisk, där data som har integritetsproblem har exkluderats. Simuleringsresultat har validerats mot elförbrukningsdata från småhus och kontorsbyggnader,  relaterade modeller från andra forskargrupper samt energistatistik från nationella databaser. Valideringen visar att modellerna kan generera elförbrukningsprofiler med rimlig noggrannhet.

Denna avhandling är en sammanläggningsavhandling bestående av fem artiklar. Artikel 1 presenterar botten-upp-simuleringsmodellen för genereringen av elförbrukningsprofiler från uppvärmning, varmvatten och apparater i småhus. Artikel 2 presenterar ett dataanalytiskt ramverk för analys av elanvändningen från uppvärmning, ventilation, och luftkonditioneringslaster (HVAC) och apparatlaster i en kontorsbyggnad. Artikel 3 presenterar en icke-homogen Markovkedjemodell för simulering av representativa närvaroprofiler i enskilda kontorsrum. Artikel  4 använder resultaten i artiklarna  2 och 3 för att beskriva en botten-upp-simuleringsmodell för generering av elförbrukningsprofiler från HVAC-laster och apparater i kontorsbyggnader. Artikel  5 använder modellen i artikel 1 för att analysera den tekniska möjligheten att använda efterfrågeflexibilitet för att lösa överbelastningsproblem i ett eldistributionsnät.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2016. xiv, 73 p.
TRITA-EE, ISSN 1653-5146 ; 2016:038
Demand Response, Flexible loads, Building stock energy-use, Bottom-up simulation models, Load profiles, Non-homogeneous Markov-chains, End-user behavior, Lumped capacitance models, HVAC system control, Smart Grid., Efterfrågeflexibilitet, Flexibla laster, Energianvändning i byggnadsstocken, Botten-upp-simuleringsmodeller, Elförbrukningsprofiler, Icke-homogena Markovkedjor, Slutanvändarbeteenden, Värmedynamikmodellering, Styrning av HVAC-laster, Smarta elnät.
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
urn:nbn:se:kth:diva-184093 (URN)978-91-7595-886-6 (ISBN)
Public defence
2016-04-22, L1, Drottning Kristinas väg 30, Stockholm, 10:00 (English)

QC 20160329

Available from: 2016-03-29 Created: 2016-03-23 Last updated: 2016-04-22Bibliographically approved

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