## 1 Introduction
Typically, the best results of the accuracy of power consumption forecast are achieved using neural network models [25; 26; 27], including recurrent [28] and deep neural networks [26; 29; 30], as well as ensemble decision trees [31; 32]. Currently, there is a lack of studies on forecasting power consumption for gas industry enterprises. However, despite the efficiency of machine learning methods, forecast accuracy can be up only to 60% due to the complexity of accounting for production processes. To address this issue, a multi-agent approach can be applied. Multi-agent methodology is a methodology for modeling production processes by decomposing them into agents. This methodology allows for investigating the behavior of real production processes by constructing loosely coupled autonomous agents that interact with each other to achieve set goals. An agent can be determined as an object that receives data from the environment and acts upon it [33]. Based on the multi-agent approach, a multi-agent system (MAS) can be created, which is defined as a system consisting of several interacting agents. The terms multi-agent system and multi-agent approach are related but refer to different aspects of studying and applying agents. When discussing the term multi-agent system, the main focus is on the system as a whole, the mechanisms of interaction between agents, and between agents and the environment [34]. The application of the multi-agent approach is associated with considering mechanisms for distributing tasks among agents, coordinating their actions, and managing their interactions to achieve individual and common goals. Thus, a multi-agent system is a type of system that consists of several interacting agents, while the multi-agent approach is a methodology for solving problems by decomposing production processes and objects into agents and their interactions. The difference lies in what the focus is on--the system itself or the methodology for designing and analyzing systems [35]. Currently, multi-agent systems (MAS) are applied to solve various optimization problems related to the operation of enterprises and systems. Some of the applications of MAS include but are not limited to the following:  
* The selection of control strategies for microgrid modes considering energy-storage systems [36; 37];
* The management of power consumption in a microgrid with photovoltaic stations, introducing a generator agent whose objective function is to minimize power purchase costs based on forecasted generation [38];
* The management of a power system with renewable energy generation objects [39];
* Processing heterogeneous information in microgrid [40]. The use of MAS for gas industry enterprises was proposed in [41; 42]. The authors describe the use of generator agents, power supply system agents, and consumer agents. Agents are used to ensure the reliable operation of the enterprise by optimizing electrical modes, network topology, and power balance. This approach allows for analyzing the operation modes of the system. However, it should be noted that these articles primarily use an object-oriented approach, as the goals of the agents, their input and output data, and the interactions between agents are not defined or described. It can be assumed that considering production process factors from the perspective of a multi-agent approach may improve the accuracy of power consumption forecasting for gas industry enterprises. Furthermore, it should be noted that current research on power consumption forecasting primarily focuses on improving forecast accuracy, while the models remain \"black boxes\" for experts. This complicates the implementation of these models in enterprises due to experts' lack of trust in the learning results of these models [43]. Studies in the field of eXplainable Artificial Intelligence (XAI) were conducted to address this issue [44; 45]. For complex models that are not interpretable, a posterior explanation can be used.