Aggregation of energy consumption forecasts across spatial levels
Using CNN-LSTM forecasts of lower spatial levels to forecast on higher spatial levels
T.W. Borst (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Luciano Cavalcante Cavalcante Siebert – Mentor (TU Delft - Interactive Intelligence)
S.K. Kuilman – Mentor (TU Delft - Interactive Intelligence)
M.M. Weerdt – Graduation committee member (TU Delft - Algorithmics)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
Bottom up load forecasting, is a technique where energy consumption forecasts are made on lower spatial levels, after which the resulting forecasts are aggregated to form forecasts of higher spatial levels. With the current move to renewable energy sources and the importance of reducing the strain on an already congested electricity grid, accurately forecasting both the location and time of future energy consumption has become more important than ever. To this end, this paper analyses the impact of applying bottom up load forecasting to different spatial levels on the electricity grid, including appliance, household, community and city spatial levels. Energy consumption data for these spatial levels were gathered from the 15-minute Texas and California datasets from Pecan Street Dataport. The results obtained in this study, suggest that energy consumption volatility at lower spacial levels and forecast difficulty at higher spacial levels, play an important role in the performance of applying the bottom up load forecasting technique to energy consumption forecasting problems.