Abstract
In this paper, a novel short-term load forecasting method amalgamated with quantile regression random forest is proposed. Comprised with point forecasting, it is capable of quantifying the uncertainty of power load. Firstly, a bespoke 2D data preprocessing taking advantage of empirical mode decomposition (EMD) is presented. It can effectively assist subsequent point forecasting models to extract spatial features hidden in the 2D load matrix. Secondly, by exploiting multimodal deep neural networks (DNN), three short-term load point forecasting models are conceived. Furthermore, a tailor-made multimodal spatial–temporal feature extraction is proposed, which integrates spatial features, time information, load, and electricity price to obtain more covert features. Thirdly, relying on quantile regression random forest, the probabilistic forecasting method is proposed, which exploits the results from the above three short-term load point forecasting models. Lastly, the experimental results demonstrate that the proposed method outperforms its conventional counterparts.
Highlights
Load Probabilistic ForecastingLoad forecasting is an important part of the planning and operation of power systems, which is essential for energy management, economic dispatching, and maintenance planning [1]
Power load forecasting methods can be categorized into point forecasting and probabilistic forecasting according to the output form [2]
The probabilistic forecasting method based on the mixed point forecasting model was implemented to obtain the probabilistic forecasting results
Summary
Load Probabilistic ForecastingLoad forecasting is an important part of the planning and operation of power systems, which is essential for energy management, economic dispatching, and maintenance planning [1]. Power load forecasting methods can be categorized into point forecasting and probabilistic forecasting according to the output form [2]. As the uncertainty of the supply side and the demand side in the power system increases, traditional deterministic power load point forecasting theory will no longer meet the new demands of the development of smart grid. Compared with traditional point forecasting, probabilistic forecasting could successfully quantify the uncertainty of power demand and provide more comprehensive information about future moments [3]. Probabilistic forecasting of the power load has become an increasingly useful technology in smart grid data analysis. Li et al [4] conducted a new exploration of interval forecasting technology and proposed a proportional coefficient method based on an extreme learning machine
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
