Characterizing the load profile in power grids by Koopman mode decomposition of interconnected dynamics

by   Ali Tavasoli, et al.

Electricity load forecasting is crucial for effectively managing and optimizing power grids. Over the past few decades, various statistical and deep learning approaches have been used to develop load forecasting models. This paper presents an interpretable machine learning approach that identifies load dynamics using data-driven methods within an operator-theoretic framework. We represent the load data using the Koopman operator, which is inherent to the underlying dynamics. By computing the corresponding eigenfunctions, we decompose the load dynamics into coherent spatiotemporal patterns that are the most robust features of the dynamics. Each pattern evolves independently according to its single frequency, making its predictability based on linear dynamics. We emphasize that the load dynamics are constructed based on coherent spatiotemporal patterns that are intrinsic to the dynamics and are capable of encoding rich dynamical features at multiple time scales. These features are related to complex interactions over interconnected power grids and different exogenous effects. To implement the Koopman operator approach more efficiently, we cluster the load data using a modern kernel-based clustering approach and identify power stations with similar load patterns, particularly those with synchronized dynamics. We evaluate our approach using a large-scale dataset from a renewable electric power system within the continental European electricity system and show that the Koopman-based approach outperforms a deep learning (LSTM) architecture in terms of accuracy and computational efficiency. The code for this paper has been deposited in a GitHub repository, which can be accessed at the following address


Short-Term Load Forecasting Using A Particle-Swarm Optimized Multi-Head Attention-Augmented CNN-LSTM Network

Short-term load forecasting is of paramount importance in the efficient ...

Submodular Load Clustering with Robust Principal Component Analysis

Traditional load analysis is facing challenges with the new electricity ...

Dynamic mode decomposition for forecasting and analysis of power grid load data

Time series forecasting remains a central challenge problem in almost al...

N-BEATS neural network for mid-term electricity load forecasting

We address the mid-term electricity load forecasting (MTLF) problem. Thi...

PSML: A Multi-scale Time-series Dataset for Machine Learning in Decarbonized Energy Grids

The electric grid is a key enabling infrastructure for the ambitious tra...

Multivariate Empirical Mode Decomposition based Hybrid Model for Day-ahead Peak Load Forecasting

Accurate day-ahead peak load forecasting is crucial not only for power d...

A joint Bayesian hierarchical model for estimating SARS-CoV-2 diagnostic and subgenomic RNA viral dynamics and seroconversion

Understanding the viral dynamics and immunizing antibodies of the severe...

Please sign up or login with your details

Forgot password? Click here to reset