# Medium- and Long-Term Load Forecasting for Power Plants Based on Causal Inference and Informer
## 1 Introduction
### Existing Research Gap  
Early power load forecasting tasks relied on the expertise and mathematical skills of practitioners [5], which had advantages in terms of model interpretability. However, in the current era of big data, such methods perform poorly in dealing with large and complex volumes of data. Data-driven forecasting techniques are gaining prominence due to the rising advancement of machine learning methods, including Seasonal Auto-Regressive Integrated Moving Average (SARIMA) [6], Support Vector Machine (SVM) [7], and iterative ResBlocks [8].  
The aforementioned methods are currently mainly applied to short-term forecasting, relying on the ability of machine learning algorithms to fit input-output variables, which often yields good results. Despite such results, when the described methods are directly applied to medium- and long-term power plant generation load (hereinafter abbreviated as load) forecasting, the results may not be satisfactory. To solve the problem of medium and long-term load forecasting, some scholars have proposed advanced methods. In one study [9], the load data were decoupled and a novel method was used to convert the tabular data into image data to finally achieve medium- and long-term load forecasting for distribution transformers. Variables such as outdoor temperature, humidity, and wind speed were used as inputs to achieve multi-time step forecasting of heating load based on Informer [10]. An improved Long Short-Term Memory (LSTM) model has also been proposed [11] and the effectiveness of the method was verified on a residential load dataset. Additionally, several researchers have expressed the belief that using a hybrid forecasting method based on the optimal combination of multiple forecasting models can help improve forecasting accuracy. A hybrid model based on Prophet and LSTM has also been proposed [12]. Through experiments, the hybrid approach was demonstrated to be able to improve the forecasting accuracy of power load.  
The current literature on medium- and long-term load forecasting is sparse, primarily due to the following difficulties:  
(1) Non-stationary data are typically more challenging to predict than stationary data. Load is a nonlinear variable, and external factors have a considerable impact on it [13]. Moreover, since load is affected by various external factors, such as ambient temperature, holidays, plant maintenance status, GDP, and other factors, it can be considered non-stationary data, that is, data of which the statistical properties and distribution patterns are constantly changing over time [14].  
(2) Medium- and long-term forecasting is more difficult compared to short-term forecasting, and errors in forecasting tend to increase as the time step for forecasting grows longer.  
(3) The correlation between various variables and load lacks qualitative analysis. The degree of influence of various variables on load is difficult to calculate quantitatively.  
The aforementioned problems constrain the performance of medium- and long-term load forecasting methods. The focus of existing timing prediction methods has been on continuously improving the model's ability to couple the correlation between output and input, while research on the causes of the coupling relationship has been neglected. In the present study, Informer was used, a novel algorithm in the field of long time-series forecasting that has shown good results on open source datasets but lacks practical applications in generation load-forecasting scenarios. Several improvements were made to Informer to adapt it to non-stationary data in real-world scenarios. In addition, the mutual information method was adopted to remove load-independent external variables. Further, the effect of different external variables on load was quantitatively investigated using causal inference as the primary method. The causal inference model was combined with the improved Informer model to obtain a hybrid model (Causal-Informer) with high interpretability and robustness.