Cluster Analysis of Daily Electricity Demand with t-SNE

• Published in: 韓國컴퓨터情報學會論文誌 Vol. 23; no. 5; pp. 9 - 14
• Main Author: Min, Yunhong
• Format: Journal Article
• Language: Korean
• Published: 2018
• Subjects: t-SNE; Dynamic Time Warping; Electricity demand; Clustering; Forecasting
• ISSN: 1598-849X; 2383-9945

For an efficient management of electricity market and power systems, accurate forecasts for electricity demand are essential. Since there are many factors, either known or unknown, determining the realized loads, it is difficult to forecast the demands with the past time series only. In this paper we perform a cluster analysis on electricity demand data collected from Jan. 2000 to Dec. 2017. Our purpose of clustering on electricity demand data is that each cluster is expected to consist of data whose latent variables are same or similar values. Then, if properly clustered, it is possible to develop an accurate forecasting model for each cluster separately. To validate the feasibility of this approach for building better forecasting models, we clustered data with t-SNE. To apply t-SNE to time series data effectively, we adopt the dynamic time warping as a similarity measure. From the result of experiments, we found that several clusters are well observed and each cluster can be interpreted as a mix of well-known factors such as trends, seasonality and holiday effects and other unknown factors. These findings can motivate the approaches which build forecasting models with respect to each cluster independently.