The potential of AI techniques to enhance conformity assessment processes has garnered attention within the sector. Traditional methods of conformity assessment suffer from a lack of critical analytics and susceptibility to subjective data analysis, human error, inefficiency, and poor data quality. Intelligent systems will improve the accuracy and consistency of assessments when evaluating the energy performance of household appliances. This paper presents some ML approaches to energy consumption anomaly detection applied to household appliances, with the potential to improve the efficiency, accuracy, and reliability of predicting energy consumption. The study proposes an intelligent system to enhance the accuracy, precision, and quality of appliance energy performance conformity assessment testing processes with minimal human intervention. Thus, providing a quality energy assessment, aiding in the efforts to reduce energy consumption in households.