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Övervakad maskininlärning för att identifiera nya kunder på energimarknaden
KTH, School of Computer Science and Communication (CSC).
KTH, School of Computer Science and Communication (CSC).
2017 (Swedish)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Supervised machine learning as a tool for identifying new customers on the energy market (English)
Abstract [en]

This paper explores alternative ways for smaller actors on the energy market to identify potential customers using publicly available data and different machine learning algorithms. During recent years, price has been considered to have the biggest impact on the behaviour of the consumers on the energy market. Since the bigger actors on the market can use their economies of scale to lower their prices, smaller actors need to find alternative ways to reach out to consumers. The machine learning algorithms in this paper will use the sales data from a small energy company, operating in Sweden and attempt to find a connection between existing customers using their demographic properties. By acquiring a deeper knowledge of what differentiates consumers that are willing to purchase energy from the energy company and the other consumers, the energy company may increase their rate of successful sales. Due to the lack of customer data avilable coupled with a lack of relevant public data, the results in this paper are not conclusive. However, it provides a baseline for future research as the results may be more reliable when the number of customers purchasing energy from The Energy Company increases.

Abstract [sv]

Det här arbetet utforskar alternativa tillvägagångssätt för för mindre aktörer på energimarknaden att identifiera nya potentiella kunder, baserat på publikt tillgänglig data som analyseras med hjälp av maskininlärningsalgoritmer. På senare år har pris ansetts vara den faktor som påverkar val av leverantör mest. Eftersom större aktörer på marknaden kan utnyttja skalfördelar kan de pressa priserna hårt, medans mindre aktörer måste finna andra vägar att vinna nya kunder. Maskininlärningsalgoritmerna i den här uppsatsen kommer att använda försäljningsdata från ett litet energibolag, som bedriver verksamhet i Sverige, med målet att hitta ett mönster mellan existerande kunder och deras demografiska data. Genom att förskaffa sig djupare kunskap om vad som differentierar kunder kan energibolaget förbättra sin försäljning. På grund av en förhållandevis liten mängd kunddata och brist på publik data gick det inte att hitta ett betydande samband mellan kunderna och deras demografiska data. Resultaten utgör dock en bra grund för fortsatt forskning då resultaten blir mer pålitliga då mer kunddata införskaffas, vilket blir en naturlig följd av att energibolagets försäljning fortsätter utvecklas.

Place, publisher, year, edition, pages
2017.
Keywords [en]
supervised machine learning, energy market
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-210782OAI: oai:DiVA.org:kth-210782DiVA, id: diva2:1119934
External cooperation
ChessIT
Educational program
Master of Science in Engineering - Industrial Engineering and Management
Supervisors
Examiners
Available from: 2017-10-16 Created: 2017-07-05 Last updated: 2018-01-13Bibliographically approved

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CiteExportLink to record
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