An Integrated Gaussian Process Modeling Framework for Residential Load Prediction

Abstract

While adding new capabilities, the distributed energy resource (DER) proliferation raises great concern about challenges such as dynamic fluctuations of voltages. For robust and efficient operational planning purposes, we propose an integrated Gaussian process (IGP) modeling framework for reliable hourly load prediction. The proposed IGP modeling framework has the following unique features: 1) the IGP utilizes not only the data streams generated by the target customer, but also those generated by relevant customers in the power system; an effective input space dimension reduction method is proposed to significantly improve the computational efficiency, while maintaining the high predictive accuracy of the IGP; and 2) an adaptive data communication rate controlling scheme is proposed to further enhance the predictive performance of the IGP by optimally and dynamically adjusting the data communication rate used for each customer under the total data communication bandwidth constraint often imposed. Taking into account the highly uncertain load and generation behaviors of DERs, the proposed IGP framework is tested on various standard IEEE test cases with load and renewable generation data collected from real-world power systems with DERs. The superiority and efficacy of the IGP are verified by our simulation results.