In order to improve the efficiency and sustainability of electricity systems, most countries worldwide are deploying advanced metering infrastructures, and in particular household smart meters, in the residential sector. This technology is able to record electricity load time series at a very high frequency rates, information that can be exploited to develop new clustering models to group individual households by similar consumptions patterns. To this end, in this work we propose three hierarchical clustering methodologies that allow capturing different characteristics of the time series. These are based on a set of "dissimilarity" measures computed over different features: quantile auto-covariances, and simple and partial autocorrelations. The main advantage is that they allow summarizing each time series in a few representative features so that they are computationally efficient, robust against outliers, easy to automatize, and scalable to hundreds of thousands of smart meters series. We evaluate the performance of each clustering model in a real-world smart meter dataset with thousands of half-hourly time series. The results show how the obtained clusters identify relevant consumption behaviors of households and capture part of their geo-demographic segmentation. Moreover, we apply a supervised classification procedure to explore which features are more relevant to define each cluster.
Dependence structure for the product of bi-dimensional finite-variance VAR(1) model components. An application to the cost of electricity load prediction errors
Postprocessing of point predictions for probabilistic forecasting of day-ahead electricity prices: The benefits of using isotonic distributional regression
Isotonic Quantile Regression Averaging for uncertainty quantification of electricity price forecasts
Doctoral School, Faculty of Management, Wrocław University of Science and Technology（沃斯克拉大学管理学院博士学院）；Department of Operations Research and Business Intelligence, Wrocław University of Science and Technology（沃斯克拉大学运筹学与商业智能系）
Optimal placement of wind farms via quantile constraint learning
Department of Statistics, University Carlos III of Madrid（统计系，卡洛斯三世大学）；UC3M-BS Institute for Financial Big Data (IFiBiD), University Carlos III of Madrid（UC3M-BS金融大数据研究所（IFiBiD），卡洛斯三世大学）；Department of Wind and Energy Systems, Technical University of Denmark (DTU)（风能与能源系统系，技术大学 of Denmark（DTU））
