Tureczek, Alexander Martin

Den stigende elektrificering af samfundet i særdeleshed transport, vil have stor indflydelse på fremtidensspidsbelastning af el nettet, og vil i perioder kunne overstige nettets kapacitet. Det er forventet atforbrugsfleksibilitet vil komme til at spille en vigtig rolle i reduktion af spidsbelastningen. Smarte elmålere,smart meters, er i stand til at aflæse forbrug på minut basis. Disse målere åbner for forbrugsmålinger på ethidtil uset detaljeniveau til brug for identificering af forbrugsmønstre og fleksibilitet. Denne afhandlingundersøger mulighederne for at anvende elforbrugsmålinger til kategorisering af forbrug med deraffølgende forbrugstyper. Dette kan assistere i udvikling af specifikke el-abonnementer som kan tilskynde tilforbrugsfleksibilitet.Gennem en systematisk litteratur gennemgang evalueres forskningsresulter i energi forbrugskategorisering.Systematikken sikre at resultaterne er reproducerbare. Litteratur gennemgangen identificerer K-Means ogHierarkisk clustering som de mest anvendte metoder til kategorisering af energi forbrug. Mere avanceredemetoder er anvendes sporadisk, men deres kompleksitet opvejer ikke de marginale forbedringer ikategorierne. Gennemgangen finder også at smart meter forbrugsdata kan anvendes til at identificereforbrugsmønstre, men at de identificerede mønstres stabilitet over tid er tvivlsom.Læring fra litteraturgennemgangen er anvendt til analyse af Danske elforbrugsdata fra mere end 32.000husstande. Resultaterne af analyserne af danske data er identiske med resultaterne fra sammenligneligeinternationale studier. Dertil har analyserne udført i denne afhandling introduceret autokorrelationfeatures til at forbedre K-Means clusteringen ved at inkludere autokorrelation. De kategorier som bliveridentificeret i de danske data er ikke unikke, grundet kategori-variation som resulterer i overlap mellemgrupperne. Det undersøges også om metoderne fra elforbrugsanalyserne direkte kan anvendes påfjernevarmeforbrugsdata. Det konkluderes at fjernvarmeforbrugsdata kan forbrugsklassificeres medsamme metoder som anvendes til elforbrugsklassificering. Resultaterne for fjernevarmedata er identiskemed resultaterne for elforbrugsdata, og konklusionerne for autokorrelation ligeså.Ydermere evaluerer denne afhandling stabiliteten af de identificerede forbrugskategorier via en nyudvikletmetode; Varatio, som er i stand til at analyserer om kategorierne er stabile over tid. Varatio anvendervarians forhold til at sammenligne forbrugskategorier over tid. Analysen af stabiliteten viser atforbrugskategorierne beregnet med K-Means er ustabile over tid.Denne afhandling konkluderer at el- og fjernvarmeforbrugsdata fra digitale smart-målere kan anvendes tilat identificere forbrugskategorier. Men at den for tiden fremherskende metode kan beregneforbrugskategorier så er den praktiske anvendelse begrænset. Der er for stor variation i de enkelte grupperhvilket resulterer i at grupperne overlapper. Dette skyldes at der er underliggende strukturer i data som deanvendte metoder ikke er i stand til at håndtere. For at kunne generere unikke forbrugskategorier er mereforskning i tidsrække klassificering nødvendigt.As society moves towards increasing electrification in areas such as transportation, the future peak electricity demand may very well exceed the capacity of the electricity grid. Consumption flexibility is expected to play an important role in peak shawing and smart meters can help analyze demand. Electricity smart meters are capable of recording consumption at very high frequency, down to the minute. These recordings allow for unprecedented consumption insights and identification of consumption patterns and flexibility. This thesis investigates the ability of electricity smart-meter consumption data to be used for consumption clustering to identify consumer types and enable diverse tariff structures and thus incentivize flexible consumption patterns. Through a systematic literature review the state of the art in smart meter consumption clustering is outlined and evaluated, the systematics of the review ensure reproducibility of the results. The review identifies that simple methods such as K-Means and Hierarchical clustering are prevailing; though more advanced methods are applied but their complexity and lack of improved cluster structures render them as unpopular choices. The review recognizes that smart-meter consumption data collected for billing purposes are applicable for clustering, but that the clusters are ambiguous, and their long-term stability is questionable. The lessons from the review are applied to a Danish electricity consumption dataset containing readings from more than 32,000 smart meters. The results obtained from the Danish data are comparable to international studies of electricity smart-meter consumption data. Furthermore, the analysis of the data introduces autocorrelation features to successfully improve the clustering potential of K-Means to include temporal dependencies. The clusters produced are still ambiguous but clustering is finer grained and within-cluster variance is reduced. It is investigated if the results from the review and the electricity data are readily applicable for clustering of smart-meter district heating consumption data. The methods used for electricity data are successfully applied to cluster consumption for district heating heat exchange stations, without change in methodology. The results are similar to those of electricity consumption clustering with equivalent conclusions regarding clustering of consumption data with temporal components. This thesis further investigates the time stability of the developed clusters by introduction of a novel methodology; Varatio able to evaluate if households are clustered together over time. Varatio applies variance ratios to compare clustering solutions. The analysis of cluster stability shows that the smart meter consumption clusters produced by K-Means are highly unstable, with stability of clusters being less than 20% of the meters.The thesis concludes that smart-meter data can be applied to identify consumption clusters, but the current prevailing methodology produce academically viable clusters with limited practical applicability. There are structures in the data that the methodology currently applied are unable to manage e.g. reduce the within cluster variance to such a degree that the clusters are uniquely defined and identifiable. Further research into methods for time series clustering is needed to control the cluster variance and enable distinct consumption clusters

Online Research Database In Technology

Analysis of High Frequency Smart Meter Energy Consumption Data

http://orbit.dtu.dk/ws/files/170743307/thesis_Alexander_Tureczek.pdf

Analysis of High Frequency Smart Meter Energy Consumption Data

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Online Research Database In Technology