Electricity Theft Detection in Smart Grids Based on Deep Neural Network | Python Final Year Project

Electricity Theft Detection in Smart Grids Based on Deep Neural Network | Python Final Year IEEE Project 2023.
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📌Project Title: Electricity Theft Detection in Smart Grids Based on Deep Neural Network.
💡Implementation Code: Python.
🔬Algorithm / Model Used: Artificial Neural Network (ANN).
🌐Web Framework: Flask.
🖥️Frontend: HTML, CSS, JavaScript.
💰Cost (In Indian Rupees): Rs.3000/.
IEEE Base paper Abstract:
Electricity theft is a global problem that negatively affects both utility companies and electricity users. It destabilizes the economic development of utility companies, causes electric hazards and impacts the high cost of energy for users. The development of smart grids plays an important role in electricity theft detection since they generate massive data that includes customer consumption data which, through machine learning and deep learning techniques, can be utilized to detect electricity theft. This paper introduces the theft detection method which uses comprehensive features in time and frequency domains in a deep neural network-based classification approach. We address dataset weaknesses such as missing data and class imbalance problems through data interpolation and synthetic data generation processes. We analyze and compare the contribution of features from both time and frequency domains, run experiments in combined and reduced feature space using principal component analysis and finally incorporate minimum redundancy maximum relevance scheme for validating the most important features. We improve the electricity theft detection performance by optimizing hyperparameters using a Bayesian optimizer and we employ an adaptive moment estimation optimizer to carry out experiments using different values of key parameters to determine the optimal settings that achieve the best accuracy. Lastly, we show the competitiveness of our method in comparison with other methods evaluated on the same dataset. On validation, we obtained 97% area under the curve (AUC), which is 1% higher than the best AUC in existing works, and 91.8% accuracy, which is the second-best on the benchmark.
REFERENCE:
LELOKO J. LEPOLESA, SHAMIN ACHARI, AND LING CHENG, (Senior Member, IEEE), “Electricity Theft Detection in Smart Grids Based on Deep Neural Network”, IEEE Access (Volume: 10), 2022.
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[@atlibachewshegaw4076]

Pls help me electricity theft detection in in smart grid based on cnn-lstm

[@jpinfotechprojects]

Kindly email your requirements to: [e-mail]

[@eeeii029kanakadurga8]

can you please tell me where can i download that dataset

[@jpinfotechprojects]

You can download the dataset from kaggle

[@eeeii029kanakadurga8]

can you please tell me the name of that dataset
@@jpinfotechprojects

[@jpinfotechprojects]

Email to: [e-mail]