Electricity theft is a kind of illegal behavior

which will lead to income loss

operating costs increase and damage to the security of electric power enterprises. The traditional electricity theft detection method is mainly a combination of data statistical analysis and manual on-site verification mode. It is characterized by high manpower cost

low positioning efficiency

and especially has major flaws in the accurate analysis of illegal electricity use. With the progress of advanced metering infrastructure advanced metering infrastructure (AMI) and new generation of artificial intelligence technology

the massive time series electricity consumption data of power consumption information collection system 2.0 has laid a better foundation for the development of electricity theft detection. For this reason

this paper firstly starts from the process of time series-based electricity theft detection method

and analyzes the intrinsic mechanism of data processing

feature extraction

binary classification and other electricity theft detection. Immediately after that

the advantages

limitations and practical impacts of artificial intelligence methods such as machine learning and deep learning in electricity theft detection technology are deeply analyzed

and the challenges they face are further sorted out

including data privacy issues

real-time processing requirements and low false alarm rates. Finally

potential solutions and emerging trends aimed at overcoming these challenges and improving the reliability of detection systems are discussed

including multi-source data fusion

few-shot learning detection

and cross-dataset detection

so as to provide some insights for the further development of AI- enabled power theft detection technology

as well as for researchers

practitioners

and policy makers in the energy field.