## 1 Introduction
Currently, posterior explanation in short-term power consumption forecasting is mainly represented by algorithms such as Local Interpretable Model-Agnostic Explanations (LIME) [46; 47] and Shapley Additive eXPlanations (SHAP) [32; 48]. The drawback of the LIME method is the need to select, configure, and train a surrogate model, as well as the implicit violation of the accuracy principle, as the hypothesis that the surrogate model's explanation corresponds to the decision-making process of the model under explanation is an untestable assumption. For an expert, displaying the features that influenced the decision-making process along with their feature importance (weights) develops trust in the system and increases the probability of successful joint operation [49]. Therefore, the SHAP-based method of additive explanation becomes relevant for the system of short-term power consumption forecast, which is installed at gas industry enterprises, where the model is used by an expert. The Shapley Additive eXPlanations algorithm is based on the theoretically optimal Shapley values from Game Theory. SHAP determines the influence of each feature on the machine learning model's results. Currently, SHAP is primarily used for the global explanation of parameter influence on model outputs in short-term power consumption forecasting [50]. Based on the literature review, there is a gap in knowledge regarding the lack of research on power consumption forecasting for gas industry enterprises. This issue is associated with the complexity of accounting for numerous production process factors. The objectives of the study are to develop a method for decomposing production processes at a gas industry enterprise using a multi-agent approach and to create a method for short-term power consumption forecasting at a gas industry enterprise based on explainable artificial intelligence. The contributions of this study are the following:  
* The MAS-based algorithm for improving the energy efficiency of production processes at gas industry enterprises was proposed;
* The necessity of the application of production process factors for increased accuracy of power consumption forecast was experimentally proven;
* A comparative analysis of different decision tree ensemble algorithms for power consumption forecasting in a gas industry enterprise with varying numbers of features was conducted;
* The possibility of enhancing the interpretability of power consumption forecasts for each hour of the day using the SHAP algorithm, considering changes in SHAP results depending on the applied machine learning model, was explored. In addition, this study covers data preprocessing. This can be of interest to researchers having problems processing the data obtained from real-life power objects with a low level of digitization of data gathering, processing, and storing. The article is organized as follows. Section 2 presents descriptions of the studied object, the proposed data-preprocessing algorithm, the proposed method for decomposing production processes at a gas industry enterprise using a multi-agent approach, and a method for short-term power consumption forecasting at a gas industry enterprise based on explainable artificial intelligence, descriptions of machine learning methods, and an approach to results interpretation. Section 3 shows the results and discussion of applying machine learning methods and SHAP to the preprocessed dataset in the short-term power consumption forecasting task. Section 4 concludes the article by describing the future prospects of the study.