Scenario Forecasting of Residential Load Profiles

Zhang, Ling; Zhang, Baosen

doi:10.1109/jsac.2019.2951973

Cited by 32 publications

(20 citation statements)

References 32 publications

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“…Similarly, a flow-based generative network is used to model image through sampling and latent variables manipulations in [29]. To accurately predict power load, a conditional flow-based model is proposed to capture the complex temporal dependency of residential load in [30].…”

Section: B Literature Reviewmentioning

confidence: 99%

Modeling Daily Load Profiles of Distribution Network for Scenario Generation Using Flow-Based Generative Network

Liao

Wang

et al. 2020

IEEE Access

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The daily load profiles modeling is of great significance for the economic operation and stability analysis of the distribution network. In this paper, a flow-based generative network is proposed to model daily load profiles of the distribution network. Firstly, the real samples are used to train a series of reversible functions that map the probability distribution of real samples to the prior distribution. Then, the new daily load profiles are generated by taking the random number obeying the Gaussian distribution as the input data of these reversible functions. Compared with existing methods such as explicit density models, the proposed approach does not need to assume the probability distribution of real samples, and can be used to model different loads only by adjusting the structure and parameters. The simulation results show that the proposed approach not only fits the probability distribution of real samples well, but also accurately captures the spatial-temporal correlation of daily load profiles. The daily load profiles with specific characteristics can be obtained by simply classification.INDEX TERMS Daily load profiles, distribution network, generative network. BIRGITTE BAK-JENSEN (Senior Member, IEEE) received the M.Sc. degree in electrical engineering and the Ph.D. degree in modeling of high voltage components from the

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Section: B Literature Reviewmentioning

confidence: 99%

Modeling Daily Load Profiles of Distribution Network for Scenario Generation Using Flow-Based Generative Network

Liao

Wang

et al. 2020

IEEE Access

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“…However, the authors do not declare the exploding likelihood and the overfitting as issues and present the spurious results without further discussion. Zhang and Zhang (2020) acknowledge that their scenarios exhibit noisy behavior that does not match the expected results. However, they attribute this noise to their conditional training approach.…”

Section: Introductionmentioning

confidence: 97%

“…However, normalizing flows are not as well established as GANs and VAEs in scenario generation yet. To the best of our knowledge, the only works using normalizing flows in the context of energy time series are Zhang and Zhang (2020) and Ge et al (2020) focusing on demand time series and Dumas et al (2021) generating PV and wind electricity time series.…”

Section: Introductionmentioning

confidence: 99%

Principal component density estimation for scenario generation using normalizing flows

Cramer

Mitsos

Tempone

et al. 2022

DCE

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Neural networks-based learning of the distribution of non-dispatchable renewable electricity generation from sources, such as photovoltaics (PV) and wind as well as load demands, has recently gained attention. Normalizing flow density models are particularly well suited for this task due to the training through direct log-likelihood maximization. However, research from the field of image generation has shown that standard normalizing flows can only learn smeared-out versions of manifold distributions. Previous works on normalizing flow-based scenario generation do not address this issue, and the smeared-out distributions result in the sampling of noisy time series. In this paper, we exploit the isometry of the principal component analysis (PCA), which sets up the normalizing flow in a lower-dimensional space while maintaining the direct and computationally efficient likelihood maximization. We train the resulting principal component flow (PCF) on data of PV and wind power generation as well as load demand in Germany in the years 2013–2015. The results of this investigation show that the PCF preserves critical features of the original distributions, such as the probability density and frequency behavior of the time series. The application of the PCF is, however, not limited to renewable power generation but rather extends to any dataset, time series, or otherwise, which can be efficiently reduced using PCA.

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“…Recently, the flow‐based generative models 30,31 have also been used for the generation of realistic scenarios. Zhang et al proposed a flow‐based conditional generative model to provide reliable and sharp scenarios for residential load 32 . To accurately capture the potential behavior of real samples, a generative network based on nonlinear independent component estimation was proposed by Ge et al to model the daily load profiles 33 …”

Section: Introductionmentioning

confidence: 99%

Day‐ahead renewable scenario forecasts based on generative adversarial networks

et al. 2020

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Summary With the increasing penetration of renewable resources such as wind and solar, especially in terms of large‐scale integration, the operation and planning of power systems are faced with great risks due to the inherent stochasticity of natural resources. Although this uncertainty can be anticipated, the timing, magnitude, and duration of fluctuations cannot be predicted accurately. In addition, the outputs of renewable power sources are correlated in space and time, and this brings further challenges for predicting the characteristics of their future behavior. To address these issues, this paper describes an unsupervised distribution learning method for renewable scenario forecasts that considers spatiotemporal correlation based on generative adversarial network (GAN), which has been shown to generate realistic time series for stochastic processes. We first utilize an improved GAN to learn unknown data distributions and model the dynamic processes of renewable resources. We then generate a large number of forecasted scenarios using stochastic constrained optimization. For validation, we use power generation data from the National Renewable Energy Laboratory wind and solar integration datasets. The simulation results show that the generated trajectories not only reflect the future power generation dynamics, but also correctly capture the temporal, spatial, and fluctuant characteristics of the real power generation processes. The experimental comparisons verify the superiority of the proposed method and indicate that it can reduce at least 50% of the training iterations of the generative model for scenario forecasts.

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Scenario Forecasting of Residential Load Profiles

Cited by 32 publications

References 32 publications

Modeling Daily Load Profiles of Distribution Network for Scenario Generation Using Flow-Based Generative Network

Modeling Daily Load Profiles of Distribution Network for Scenario Generation Using Flow-Based Generative Network

Principal component density estimation for scenario generation using normalizing flows

Day‐ahead renewable scenario forecasts based on generative adversarial networks

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