In the context of the global energy crisis and growing energy consumption, effective energy management is becoming one of the key tasks for modern economies. Anomalies in energy consumption systems can negatively affect the stability and efficiency of both individual enterprises and entire regions. These anomalies can occur as a result of various factors, including technological disruptions, changes in consumer habits, and external influences such as weather conditions. Therefore, the development of methods that effectively detect and analyze anomalies is of paramount importance to ensure the sustainability of energy systems. In recent years, Soft Computing methods such as fuzzy logic, genetic algorithms, and artificial neural networks have offered new approaches to solving problems related to anomaly detection. These methods allow you to work with vague and fuzzy data, which is common in energy consumption systems. Due to their flexibility and adaptability, Soft Computing methods can not only improve the accuracy of anomaly detection, but also provide opportunities for forecasting and optimizing energy consumption. This article discusses the process of detecting anomalies, as well as practical examples of the application of soft calculation methods in the field of energy consumption.