A scalable load forecasting system for low voltage grids

Abstract

A recent research trend is driven to increase the monitoring and control capabilities of low voltage networks. This paper describes a probabilistic forecasting methodology based on kernel density estimation and that makes use of distributed computing techniques to create a highly scalable forecasting system for LV networks. The results show that the proposed algorithm outperforms three benchmark models (one for point forecast and two for probabilistic forecasts) and demonstrate the applicability of the distributed in-memory computing solution for a practical operational scenario. The ultimate goal is to integrate information about net-load forecasts in power flow optimization frameworks for low voltage networks in order to solve technical constraints with the available home energy management system flexibility.

Probabilistic Low-Voltage State Estimation Using Analog-Search Techniques

“…Firstly, the final pdf is determined by properly normalizing the estimated pdf, so that the integral is equal to one. Secondly, the cdf is obtained using numerical integration through the "normalized" pdf [17]. Once the cdf is estimated, extracting the quantiles is straightforward and computationally cheap.…”

Multi-period flexibility forecast for low voltage prosumers

“…Exponential functions are used for the distribution's tails as described in [47]. For load time series, probabilistic forecasts are generated with conditional kernel density estimation [48].…”

Predictive management of low-voltage grids

“…The core modules of this component are as follows: † KDE forecast modelstatistical method that combines conditional kernel density estimation with locally learning methods to produces point and probabilistic forecasts (represented by probability density functions and/or a set of quantiles) for a time horizon constrained by the time horizon of the weather prediction data. More details can be found in [2]. † In-memory and distributed computing taskscombination of Gearman (gearman.org) to distribute tasks to multiple processes/ computers and Memcached (memcached.org) to store time series data in cache for quick access by KDE, without going through layers of parsing or disk I/O.…”

“…The statistical forecast method is non-parametric and essentially operates considering the historical data for analogue past situations, combining and weighting them based on the new measurements [2,3]. When establishing a similarity criterion (a distance function), only a percentage (p r ) of the total historical data are used for the density estimation.…”