# Medium- and Long-Term Load Forecasting for Power Plants Based on Causal Inference and Informer
## 1 Introduction  
### Background of the Present Study  
With the development of the economy and the improvement of people's quality of life, a stable and reliable power supply becomes increasingly important to society. The full implementation of power market reform has led to the establishment of a power market trading system and intensified efforts to strengthen power market trading. Using Jiangsu Province in China as an example, according to an official document [1], Jiangsu Province has issued trading rules [2] for electricity, which allow power plants to participate in the electricity market and independently negotiate power tariffs with other market players. Therefore, it is essential for power plants to study generation load forecasts in order to develop more accurate trading strategies. For example, based on generation load forecasts, power plants can adjust their bidding strategies and develop operational strategies such as the purchase of raw materials and staffing in advance, which can reduce the production costs of power generation companies and increase their profits. Based on the length of the forecast, power load forecasts can be classified into the following categories: long-term series forecasts, medium- and long-term series forecasts,medium-term forecasts, and short-term forecasts [3]. Power plants frequently engage in market trading and set purchase targets for a minimum duration of one month. Thus, in the present study, load for at least one month was predicted, which belongs to medium- and long-term load forecasting. The objective of the present study is to propose an accurate medium- and long-term load forecasting method to assist power plants in making informed decisions regarding the development of strategies. Load forecasting involves integrating historical data with other relevant factors to predict load values for a specific period in the future. Load forecasting is a challenging task that is influenced by external factors such as holidays, seasons, and weather, making it non-linear and non-stationary in its typical characteristics. There are four main categories of factors which can have an impact on load: economic factors, time factors, natural factors, and uncertainties [4]. The rapid growth of the market and the government's macroeconomic regulation directly impact load. In economically developed regions, the presence of a significant number of industrial units and employees leads to higher electricity consumption compared to economically underdeveloped regions, thereby affecting load. Additionally, the industrial structure also impacts electricity consumption, and for regions with a high proportion of manufacturing industries, their electricity consumption is also greater. The time factor refers to the longer time scales in which load trends usually show cyclical and regular changes, such as holidays and seasonal variations. Obviously, load prediction is affected by natural factors such as ambient temperature, wind speed, and humidity. Moreover, its relevance is more obvious in certain extreme natural environments, such as cold winters and hot summers, which have a more significant impact on load variations. Uncertainties are unpredictable future events that affect the normal operation of the power system. The performance of proposed method was evaluated in comparison with popular medium- and long-term load forecasting methods. The innovations and key contributions of the present study are as follows:  
1. Causal inference was introduced into the time series prediction scenario to enhance the interpretability and robustness of the model. To the best of my knowledge, this is the first time that causal learning has been introduced in the field of medium- and long-term load forecasting to improve forecasting. 2. The Informer algorithm was improved to make it more suitable for predicting non-stationary data in real scenarios. 3. A novel prediction model, Causal-Informer, was proposed and compared with other popular forecasting approaches.