Clustering-Based Improvement of Nonparametric Functional Time Series Forecasting: Application to Intra-Day Household-Level Load Curves

Abstract

Energy suppliers are facing ever increasing competition, so that factors like quality and continuity of offered services must be properly taken into account. Furthermore, in the last few years, many countries are interested in renewable energies such as solar and wind. Renewable energy resources are mainly used for environmental and economic reasons such as reducing the carbon emission. It might also be used to reinforce the electric network especially during high peak periods. However, the injection of such energy resources in the low-voltage (LV) network can leads to high voltage constrains. To overcome this issue, one can motivate customers to use thermal or electric storage devices during high-production periods of PV to foster the integration of renewable energy generation into the network. In this paper, we are interested in forecasting household-level electricity demand which represents a key factor to assure the balance supply/demand in the LV network. A novel methodology able to improve short term functional time series forecasts has been introduced. An application to the Irish smart meter data set showed the performance of the proposed methodology to forecast the intra-day household level load curves.