1 A Multi-task End-to-End Multivariate Long-sequence Time Series Prediction Model for Load Forecasting Ziyuan Zhang, Yuanzheng Li, Senior Member, IEEE, Yang Li, Senior Member, IEEE, Yun Liu, Senior Member, IEEE, Xiangpeng Xie, Senior Member, IEEE and Zhigang Zeng, Fellow, IEEE Abstract—With the increasing complexity of the power system, and the growing global demand for electricity, accurate and effective forecasting of electricity load, electricity prices, and related meteorological features becomes increasingly crucial. However, existing methods tend to use only one element, for example, electricity load, as their prediction target instead of generating multiple indexes (e.g., electricity load, electricity prices, and related meteorological features) predictions jointly. This is insufficient for electricity producers to make decisions. While some time series forecasting methods can predict multiple indicators simultaneously, they often overlook the correlations among these features. These approaches miss the potential performance improvement that modeling correlations among multiple series could bring to the prediction results. To address the aforementioned concerns, this study presents a model named PatchGRU. Once trained, PatchGRU is capable of predicting long-sequences of electricity loads, electricity prices, and related meteorological features in a single forward propagation. The model differentiates between time-variant and time-invariant components using a Temporal Variance Separator, processing them separately. For the time-variant part, the network uses the proposed multi-scale patch input in place of traditional point inputs, then feeds it through the Gate Recurrent Unit (GRU), mean supervision, and Local-Global Feature Interaction modules, ultimately generating the forecast results. Moreover, this model relies solely on GRU and Multilayer Perceptrons (MLPs) and is much simpler than the Transformer structure. Experimental results show that the proposed model outperforms state-of-the-art models in different regions, time periods, and sampling frequencies, achieving the best results in forecasts with a maximum lead time of up to 30 days. Index Terms—Load forecasting, Time series forecasting, Long- sequence, Neural networks, Deep learning I. I NTRODUCTION A S global energy systems expand and electricity con- sumption patterns diversify, the controllability of power systems decreases. Meteorological factors like temperature and The work is supported by the National Natural Science Foundation of China under Grant No. 62422308 and No. 52377081. Ziyuan Zhang, Yuanzheng Li and Zhigang Zeng are with the School of Artificial Intelligence and Automation, Huazhong University of Sci- ence and Technology, Wuhan 430074, China (e-mail:zhang zy@hust.edu.cn; Yuanzheng Li@hust.edu.cn; zgzeng@hust.edu.cn). Yang Li is with the School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China (e-mail: liyang@neepu.edu.cn). Yun Liu is with the School of Electric Power Engineering, South China University of Technology, Guangzhou 510641, China (e-mail: li- uyun19881026@gmail.com). Xiangpeng Xie is with the Institute of Advanced Technology, Nanjing University of Posts and Telecommunications, Nanjing 210023, China (e-mail: xiexiangpeng1953@163.com). humidity significantly impact electricity load, while complex factors like electricity prices interact with load in deeper, un- derlying ways. With enhanced data accessibility, constructing an accurate and integrated forecasting system has become feasible [1], [2]. Considerable research has been conducted on electricity load forecasting, but two main issues remain. First, most studies focus on short-term load forecasts, while long-term predictions could better support system scheduling and man- agement. Second, many models predict only a single load variable. In practice, forecasting multiple variables—such as electricity prices, regional renewable energy data (wind speed, solar irradiance), and meteorological features—could reveal hidden relationships and provide managers with more reliable decision-making information, thereby facilitating subsequent operations for power system [3]–[5]. Forecasting electricity load and its related features can be approached as a time series problem, using either statistical or machine learning- based methods. Statistical methods remain widely used in electricity load forecasting. Early approaches included the naive forecast (NF) [6], which uses historical data as forecast values. Common methods include the Autoregressive (AR) model [7], Autore- gressive Moving Average (ARMA) [8], and Autoregressive Integrated Moving Average (ARIMA) [9]. These models, while advantageous for their fast computation and low data requirements, are limited by their linear nature, making them less effective for complex, nonlinear load patterns [10]. Machine learning methods are also applied to forecast elec- tricity load, prices, and meteorological features. Some scholars use Support Vector Machines (SVM) with optimized param- eters [11], [12]. As machine learning advances, regression algorithms such as eXtreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) gained popularity. These models excel in nonlinear mapping and generally offer better predictive accuracy without assuming data characteristics. However, they often lack strong support for multivariate and multi-step forecasting. Following the substantial achievements of artificial neural networks in computer vision and natural language processing, many researchers have also attempted to use various neural network architectures for time series forecasting. Some of these have already been applied to the prediction of electricity load, while others have not yet been utilized in this field. From the perspective of neural network architectures, existing This article has been accepted for publication in IEEE Transactions on Smart Grid. This is the author's version which has not been fully edited and content may change prior to final publication. Citation information: DOI 10.1109/TSG.2025.3605653 © 2025 IEEE. All rights reserved, including rights for text and data mining and training of artificial intelligence and similar technologies. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information. Authorized licensed use limited to: Tamkang Univ.. Downloaded on October 10,2025 at 14:41:36 UTC from IEEE Xplore. Restrictions apply.