Daniel Bonilla Licea presents Experimental investigation of deep learning for residential electric load forecasting

On 2023-02-28 11:00:00 at E112, Karlovo náměstí 13, Praha 2
Experimental investigation of variational mode decomposition and deep learning
for short-term multi-horizon residential electric load forecasting

With the booming growth of advanced digital technologies, it has become
for users as well as distributors of energy to obtain detailed and timely
information about the electricity consumption of households. These technologies
can also be used to forecast the household’s electricity consumption (a.k.a.
the load). In this paper, Variational Mode Decomposition and deep learning
techniques are investigated as a way to improve the accuracy of the load
forecasting problem. Although this problem has been studied in the literature,
selecting an appropriate decomposition level and a deep learning technique
providing better forecasting performance have garnered comparatively less
attention. This study bridges this gap by studying the effect of six
decomposition levels and five distinct deep-learning networks. The raw load
profiles are first decomposed into intrinsic mode functions using the
Variational Mode Decomposition in order to mitigate their non-stationary
Then, day, hour, and past electricity consumption data are fed as a
three-dimensional input sequence to a four-level Wavelet Decomposition Network
model. Finally, the forecast sequences related to the different intrinsic mode
functions are combined to form the aggregate forecast sequence. The proposed
method was assessed using load profiles of five Moroccan households from the
Moroccan buildings’ electricity consumption dataset (MORED) and was
benchmarked against state-of-the-art time-series models and a baseline
persistence model.

The seminar will last 40 min approximately, with 20 min for questions and
Responsible person: Petr Pošík