학술논문
Short-term Forecasting of Non-Conforming Net Load Using a Fusion Model with Machine Learning and Deep Learning Methods
Document Type
Conference
Author
Source
2023 9th International Conference on Control, Decision and Information Technologies (CoDIT) Control, Decision and Information Technologies (CoDIT), 2023 9th International Conference on. :2414-2419 Jul, 2023
Subject
Language
ISSN
2576-3555
Abstract
Short-term forecasting of non-conforming net load (STFNL) plays a vital role for operating a power system in secure and efficient manner. However, power system load consumption is affected by a variety of external factors and thus includes high levels of volatilities. These volatilities cause STFNL to be a challenging task and inaccurate as more distributed energy resources (DERs) continue to integrate into the power grid. Estimating the average hourly locational distribution of system loads becomes a constant daily challenge to transmission system operators as more non-visible DERs are connected to the distribution system. This paper proposes two commonly used machine-learning and deep learning methods used for load forecasting, i.e., the ensemble bagged and the long short-term memory neural network method. The advantages, features and applications of these methods are used to propose a fusion forecasting model that improves the forecasting accuracy. Additionally, data engineering and preprocessing options are used to increase the accuracy of the proposed model. A comparative study based on real-world transmission grid net load data is performed to verify that the proposed methodology is capable of reaching a relatively higher forecasting accuracy with lower error indices.