TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Walser, Thilo
A1  - Reisinger, Martin
A1  - Hartmann, Niklas
A1  - Dierolf, Christian
A1  - Sauer, Alexander
ED  - Imboden, Christoph
ED  - Bošnjak, Davor
ED  - Friedrich, Andreas K.
ED  - Hatziargyriou, Nikos
ED  - Kudela, Thomas
ED  - Nucci, Carlo Alberto
ED  - Schwark, Bastian
ED  - Svendstrup-Bjerre, Andreas
ED  - Ziegler, Sebastian
ED  - Hock, James
ED  - Moore, Fiona
ED  - Spirig, Michael
T1  - Readiness of Short-term Load Forecasting Methods for their Deployment on Company Level
T2  - Proceedings of GSM 2020
N2  - Short-term load forecasting (STLF) has been playing a key role in the electricity sector for several decades, due to the need for aligning energy generation with the demand and the financial risk connected with forecasting errors. Following the top-down approach, forecasts are calculated for aggregated load profiles, meaning the sum of singular loads from consumers belonging to a balancing group. Due to the emerging flexible loads, there is an increasing relevance for STLF of individual factories. These load profiles are typically more stochastic compared to aggregated ones, which imposes new requirements to forecasting methods and tools with a bottom-up approach. The increasing digitalization in industry with enhanced data availability as well as smart metering are enablers for improved load forecasts. There is a need for STLF tools processing live data with a high temporal resolution in the minute range. Furthermore, behin-the-meter (BTM) data from various sources like submetering and production planning data should be integrated in the models. In this case, STLF is becoming a big data problem so that machine learning (ML) methods are required. The research project “GaIN” investigates the improvement of the STLF quality of an energy utility using BTM data and innovative ML models. This paper describes the project scope, proposes a detailed definition for a benchmark and evaluates the readiness of existing STLF methods to fulfil the described requirements as a reviewing paper. 
The review highlights that recent STLF investigations focus on ML methods. Especially hybrid models gain more and more importance. ML can outperform classical methods in terms of automation degree and forecasting accuracy. Nevertheless, the potential for improving forecasting accuracy by the use of ML models depends on the underlying data and the types of input variables. The described methods in the analyzed publications only partially fulfil the tool requirements for STLF on company level. There is still a need to develop suitable ML methods to integrate the expanded data base in order to improve load forecasts on company level.
Y1  - 2020
SN  - 978-3-905592-81-8
SB  - 978-3-905592-81-8
U6  - https://doi.org/10.5281/zenodo.4284325
DO  - https://doi.org/10.5281/zenodo.4284325
SP  - 89
EP  - 103
S1  - 15
CY  - Luzern
ER  -