Autors: Percuku, A. S., Minkovska, D. V., Stoyanova, L. Y.
Title: Big Data And Time Series Use In Short Term Load Forecasting In Power Transmission System
Keywords: Short Term Load Forecasting, Big Data, Neo4j, Time Series

Abstract: Short term load forecasting plays important role in power transmission system planning and operations, and helps to make better decisions such as in maintenance planning, identify contingencies, calculate power flows, load switching and market. The aim of this paper is to present a model using Neo4j graph technology, as a Big Data NoSQL data store, in combination with Time Series method to forecast the load in short term basis for 24 hours. As a case study has been used south part of Kosovo’s power transmission system substation named SS Prizreni 2 (220kV/110kV). The historical data in that region have been used for load consumption and weather’s parameters, such as temperature, humidity and wind speed, captured from sensors every 15 minutes, and outages in a period of two years 2016/2017. This paper proposes a model which is developed using graph technology - Neo4j, to process and store those large amounts of data. Then, the Cypher query language and the concept of path on graph data

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    Вид: пленарен доклад в международен форум, публикация в реферирано издание, индексирана в Scopus