High-resolution peak demand estimation using generalized additive models and deep neural networks

Berrisch, Jonathan; Narajewski, Michał; Ziel, Florian

doi:10.1016/j.egyai.2023.100236

Cited by 4 publications

(2 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…More advanced machine learning (ML) and nonlinear methods have been often utilized in order to forecast power demand more accurately. For instance, RF (random forest) [5], ANNs (artificial neural networks) [6], K-Nearest Neighbors (KNNs) [7], support vector machines (SVMs) [7,8], and gradient boosting machine (GBM) [9] are quite commonly applied for energy modeling. The analysis of energy consumption focused on identification of the energy demand sources and other external factors which influence consumption became very important, including weather conditions, season, holiday periods, and economics [5].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Leading Point Multi-Regression Model for Detection of Anomalous Days in German Energy System

Karpio,

Łukasiewicz,

Ząbkowski

2024

Energies

View full text Add to dashboard Cite

In this article, the Leading Point Multi-Regression model was applied to identify days with anomalous energy consumption profiles. The data for the analysis come from the German energy system and they represent the hourly energy demand observed between 2006 and 2015. Days with abnormal daily profiles were identified based on the statistical analysis of the errors observed for the model. The accuracy of the model is very high and comparable with other models, e.g., LSTM, K-means, Recurrent NN, and tree-based ML methods. However, these methods rely on external factors (e.g., humidity, temperature, and sunshine) impacting energy consumption while our model uses only the energy consumption at specific fixed hours, regardless of external factors, thus being universal. Days with anomalous energy consumption profiles were identified as days related to celebration of New Year’s Eve and the New Year. Also, anomalies were identified for some other days, which were not that obvious, including Good Friday, National Day of Mourning, and, interestingly, the day of the Germany–Turkey match during the European Championship in 2008.

show abstract

Section: Introductionmentioning

confidence: 99%

“…That technique is further extended with CART (Classification and Regression Trees) and the KNN classifier. In other work [6], generalized combined additive models and deep ANN are used to identify high-resolution peak loads. Another important issue in the modeling of power demand is the identification of outliers [15].…”

Section: Introductionmentioning

confidence: 99%

Leading Point Multi-Regression Model for Detection of Anomalous Days in German Energy System

Karpio,

Łukasiewicz,

Ząbkowski

2024

Energies

View full text Add to dashboard Cite

show abstract

Detection of Anomalous Days in Energy Demand Using Leading Point Multi-regression Model

Karpio

Łukasiewicz

2023

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Using conditional Invertible Neural Networks to perform mid‐term peak load forecasting

Heidrich,

Hertel,

Neumann

et al. 2024

IET Smart Grid

View full text Add to dashboard Cite

Measures for balancing the electrical grid, such as peak shaving, require accurate peak forecasts for lower aggregation levels of electrical loads. Thus, the Big Data Energy Analytics Laboratory (BigDEAL) challenge—organised by the BigDEAL—focused on forecasting three different daily peak characteristics in low aggregated load time series. In particular, participants of the challenge were asked to provide long‐term forecasts with horizons of up to 1 year in the qualification. The authors present the approach of the KIT‐IAI team from the Institute for Automation and Applied Informatics at the Karlsruhe Institute of Technology. The approach to the challenge is based on a hybrid generative model. In particular, the authors use a conditional Invertible Neural Network (cINN). The cINN gets the forecast of a sliding mean as representative of the trend, different weather features, and calendar information as conditioning input. By this, the proposed hybrid method achieved second place overall and won two out of three tracks of the BigDEAL challenge.

show abstract

High-resolution peak demand estimation using generalized additive models and deep neural networks

Cited by 4 publications

References 33 publications

Leading Point Multi-Regression Model for Detection of Anomalous Days in German Energy System

Leading Point Multi-Regression Model for Detection of Anomalous Days in German Energy System

Detection of Anomalous Days in Energy Demand Using Leading Point Multi-regression Model

Using conditional Invertible Neural Networks to perform mid‐term peak load forecasting

Contact Info

Product

Resources

About