Uncertain Interval Forecasting for Combined Electricity-Heat-Cooling-Gas Loads in the Integrated Energy System Based on Multi-Task Learning and Multi-Kernel Extreme Learning Machine
نویسندگان
چکیده
The accurate prediction of electricity-heat-cooling-gas loads on the demand side in integrated energy system (IES) can provide significant reference for multiple planning and stable operation IES. This paper combines multi-task learning (MTL) method, Bootstrap improved Salp Swarm Algorithm (ISSA) multi-kernel extreme machine (MKELM) method to establish uncertain interval model loads. ISSA introduces dynamic inertia weight chaotic local searching mechanism into basic SSA improve speed avoid falling optimum. MKELM is established by combining RBF kernel function Poly integrate superior ability generalization two functions. Based model, weather, calendar information, social–economic factors, historical load are selected as input variables. Through empirical analysis comparison discussion, we obtain: (1) results workday better than those holiday. (2) Bootstrap-ISSA-MKELM based MTL has performance that STL method. (3) comparing discover combined prediction.
منابع مشابه
An Online Q-learning Based Multi-Agent LFC for a Multi-Area Multi-Source Power System Including Distributed Energy Resources
This paper presents an online two-stage Q-learning based multi-agent (MA) controller for load frequency control (LFC) in an interconnected multi-area multi-source power system integrated with distributed energy resources (DERs). The proposed control strategy consists of two stages. The first stage is employed a PID controller which its parameters are designed using sine cosine optimization (SCO...
متن کاملMulti-task Multiple Kernel Learning
This paper presents two novel formulations for learning shared feature representations across multiple tasks. The idea is to pose the problem as that of learning a shared kernel, which is constructed from a given set of base kernels, leading to improved generalization in all the tasks. The first formulation employs a (l1, lp), p ≥ 2 mixed norm regularizer promoting sparse combinations of the ba...
متن کاملMultiple Kernel Multi-Task Learning
Recently, there has been a lot of interest around multi-task learning (MTL) problem with the constraints that tasks should share a common sparsity profile. Such a problem can be addressed through a regularization framework where the regularizer induces a joint-sparsity pattern between task decision functions. We follow this principled framework and focus on lp−lq (with 0 ≤ p ≤ 1 and 1 ≤ q ≤ 2) ...
متن کاملImproving Multi-Instance Multi-Label Learning by Extreme Learning Machine
Multi-instance multi-label learning is a learning framework, where every object is represented by a bag of instances and associated with multiple labels simultaneously. The existing degeneration strategy-based methods often suffer from some common drawbacks: (1) the user-specific parameter for the number of clusters may incur the effective problem; (2) SVM may bring a high computational cost wh...
متن کاملExtreme Learning Machine for Multi-Label Classification
Xia Sun 1,*, Jingting Xu 1, Changmeng Jiang 1, Jun Feng 1, Su-Shing Chen 2 and Feijuan He 3 1 School of Information Science and Technology, Northwest University, Xi’an 710069, China; [email protected] (J.X.); [email protected] (C.J.); [email protected] (J.F.) 2 Computer Information Science and Engineering, University of Florida, Gainesville, FL 32608, USA; [email protected] 3 Department o...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Mathematics
سال: 2021
ISSN: ['2227-7390']
DOI: https://doi.org/10.3390/math9141645