# Short-Term Power Load Forecasting Using an Improved Model Integrating GCN and Transformer
## 1 Introduction  
Driven by the global wave of sustainable development, China has officially initiated the implementation process of its \"dual carbon\" strategy, and the energy industry is undergoing profound transformation and reform. Against this backdrop, building an accurate energy demand forecasting system has become a core supporting element for energy enterprises to optimize the supply structure, achieve efficient resource allocation, and control operating costs. Electricity, as the central carrier of the modern energy system, has special strategic value in demand forecasting. Different from other forms of energy, the physical characteristic of electricity being used immediately upon generation determines the extreme importance of the balance between supply and demand. The dynamic evolution of social production and living patterns may cause instantaneous fluctuations in electricity demand,and this characteristic amplifies the risk of resource waste caused by the mismatch between supply and demand. Establishing a high-precision power load forecasting mechanism is becoming a key approach for power enterprises to solve the problem of resource scheduling: through forward-looking demand analysis, enterprises can formulate more refined power generation plans, implement differentiated resource allocation strategies, and establish a demand-side response regulation mechanism, ultimately achieving a qualitative improvement in the utilization efficiency of power resources. The construction of this predictive ability is not only related to the operational efficiency of individual enterprises but will also reshape the value creation model of the entire power industry. When precise demand insight is deeply integrated with intelligent dispatching technology, the power supply system will have a stronger and flexible adaptability, which can not only meet the dynamic demands of economic and social development but also effectively reduce system redundancy costs, laying an important data foundation for building a new type of power system. Power load forecasting refers to the process by which researchers predict the power demand at a certain future time through the use of mathematical models. According to the length of the prediction time step, the power load forecasting task can be divided into four types [1]: ultra-short-term predictions focus on time granularities ranging from minutes to hours; short-term predictions are typically made on a daily or weekly basis; medium-term predictions cover time spans from weeks to months; and long-term predictions are strategic judgments based on an annual basis. This time-domain division system is deeply consistent with the operational characteristics of the power system. Compared to the fundamental supporting role of medium-term forecasts for production plans, short-term forecast results directly affect the start-up and shutdown decisions of generating units and the allocation of reserve capacity. In particular, the precise analysis and judgment of short-term predictions have become the key basis for power generation enterprises to optimize fuel procurement strategies and reduce start-stop losses. At the same time, it also provides important technical support for power grid dispatching institutions to implement peak-valley regulation and demand response. Since power load forecasting was proposed, the academic community has established a diversified methodological system. Due to the typical multiple uncertainty characteristics of the power load sequence, the existing research mainly follows two technical paths: traditional statistical models and deep learning architectures [2]. At the traditional methodological level, the [PERSON] team developed a non-seasonal prediction model and a sliding window algorithm based on the ARIMA framework, which were successfully applied to the power demand prediction scenario [3]. [PERSON] achieved the fitting and optimization of the load curve by improving the ARMA model [4].