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

10/08/2020
by   Daniel Dylewsky, et al.
0

Time series forecasting remains a central challenge problem in almost all scientific disciplines, including load modeling in power systems engineering. The ability to produce accurate forecasts has major implications for real-time control, pricing, maintenance, and security decisions. We introduce a novel load forecasting method in which observed dynamics are modeled as a forced linear system using Dynamic Mode Decomposition (DMD) in time delay coordinates. Central to this approach is the insight that grid load, like many observables on complex real-world systems, has an "almost-periodic" character, i.e., a continuous Fourier spectrum punctuated by dominant peaks, which capture regular (e.g., daily or weekly) recurrences in the dynamics. The forecasting method presented takes advantage of this property by (i) regressing to a deterministic linear model whose eigenspectrum maps onto those peaks, and (ii) simultaneously learning a stochastic Gaussian process regression (GPR) process to actuate this system. Our forecasting algorithm is compared against state-of-the-art forecasting techniques not using additional explanatory variables and is shown to produce superior performance. Moreover, its use of linear intrinsic dynamics offers a number of desirable properties in terms of interpretability and parsimony.

READ FULL TEXT

page 1

page 6

research
11/23/2020

Analysis of Empirical Mode Decomposition-based Load and Renewable Time Series Forecasting

The empirical mode decomposition (EMD) method and its variants have been...
research
11/04/2019

Application of Gaussian Process Regression to Koopman Mode Decomposition for Noisy Dynamic Data

Koopman Mode Decomposition (KMD) is a technique of nonlinear time-series...
research
10/09/2019

Electric Load and Power Forecasting Using Ensemble Gaussian Process Regression

We propose a new forecasting method for predicting load demand and gener...
research
04/16/2023

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

Electricity load forecasting is crucial for effectively managing and opt...
research
07/14/2023

Benchmarks and Custom Package for Electrical Load Forecasting

Load forecasting is of great significance in the power industry as it ca...
research
03/11/2023

Machine Learning Enhanced Hankel Dynamic-Mode Decomposition

While the acquisition of time series has become increasingly more straig...
research
01/17/2020

Predictability limit of partially observed systems

Applications from finance to epidemiology and cyber-security require acc...

Please sign up or login with your details

Forgot password? Click here to reset