/10Qs_load_forecasting

Companion notebooks for the B&E paper on 'Ten questions concerning data-driven modelling and forecasting of operational energy demand at building and urban scale'

Primary LanguageJupyter NotebookGNU Affero General Public License v3.0AGPL-3.0

10 questions regarding data-driven building and urban electricity load forecasting

Companion notebooks for the B&E paper on 'Ten questions concerning data-driven modelling and forecasting of operational energy demand at building and urban scale'.

The repository contains two distinct notebooks organized in two different folders. The Belgian case focuses on load forecasting on a country level, while the London case focuses on building and urban level forecasts. Both notebooks utilize different methodologies in making the forecasts, even though they share many commonalities and explore similar questions. Parts of the London dataset are too large to be hosted directly on Github, but can be found at https://www.kaggle.com/datasets/patrick0302/10qs-load-forecasting-london-dataset.

We hope the reader will find the notebooks useful, and plan to continue building further on these notebooks by including more methods and cases in the coming months.