Variational Autoencoder-based High-dimensional Feature Extraction for Economic Analysis of Power Cost Data
Abstract
The purpose of this paper is to extract the characteristics of power cost data using a deep learning model and to evaluate and predict the cost structure, profitability, and future development trends of power enterprises by combining economic analysis methods. Firstly, the paper innovatively employs a Variational Autoencoder for feature extraction. This model extracts low-dimensional latent representations of the data through an encoder and reconstructs the data through a decoder, retaining key structural information. The dataset used here consists of 1,800 records, which include costs, revenues, output, and energy consumption data from power companies, covering multiple enterprises and time periods. Secondly, during the model training process, optimization is performed with a learning rate of 0.001, a batch size of 64, and 50 training epochs. Performance comparisons under different hyperparameter combinations indicate that the model with 256 hidden nodes in both the encoder and decoder layers yields the best performance. Lastly, economic analysis methods, such as cost-benefit analysis and economic forecasting, are applied to assess and predict the profitability and future trends of the power companies. The specific results show that the reconstruction error of this model is 0.032, and the KL divergence is 0.006. In terms of refined economic analysis, the net profit predicted by the model reaches 5.36 million yuan, with a prediction accuracy of 93.5%. In terms of robustness, although the prediction accuracy fluctuates slightly, it remains high overall, and both the training time and prediction time show stability. Moreover, testing on multiple datasets from sources such as University of California, Irvine, Kaggle, and government open data platforms shows that the model's prediction accuracy remains between 92.3% and 94.2%, with stable training and prediction times, demonstrating its strong generalization ability. The proposed model offers several advantages. Overall, this paper presents a novel approach and method for economic analysis and decision-making in power enterprises, which holds significant practical value.
Full Text:
PDFReferences
Wang, J., & Chen, Y. (2021). Adaboost-based integration framework coupled two-stage feature extraction with deep learning for multivariate exchange rate prediction. Neural Processing Letters, 53(6), 4613-4637.
Cui, B., Weng, Y., & Zhang, N. (2022). A feature extraction and machine learning framework for bearing fault diagnosis. Renewable Energy, 191, 987-997.
Wang, Y., Duan, X., Zou, R., Zhang, F., Li, Y., & Hu, Q. (2023). A novel data-driven deep learning approach for wind turbine power curve modeling. Energy, 270, 126908.
Malekloo, A., Ozer, E., AlHamaydeh, M., & Girolami, M. (2022). Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights. Structural Health Monitoring, 21(4), 1906-1955.
Adil, M., Javaid, N., Qasim, U., Ullah, I., Shafiq, M., & Choi, J. G. (2020). LSTM and bat-based RUSBoost approach for electricity theft detection. Applied Sciences, 10(12), 4378.
Aslam, Z., Javaid, N., Ahmad, A., Ahmed, A., & Gulfam, S. M. (2020). A combined deep learning and ensemble learning methodology to avoid electricity theft in smart grids. Energies, 13(21), 5599.
Hussain, S., Mustafa, M. W., Jumani, T. A., Baloch, S. K., Alotaibi, H., Khan, I., & Khan, A. (2021). A novel feature engineered-CatBoost-based supervised machine learning framework for electricity theft detection. Energy Reports, 7, 4425-4436.
Guo, X., Shan, Q., Zhang, Z., & Qu, Z. (2023). Visual extraction of refined operation mode of new power system based on IPSO-kmeans. Electronics, 12(10), 2326.
Zhang, J., Yu, Y., Zhang, L., Chen, J., Wang, X., & Wang, X. (2023). Dig information of nanogenerators by machine learning. Nano Energy, 108656.
Fu, X., Wu, X., Zhang, C., Fan, S., & Liu, N. (2022). Planning of distributed renewable energy systems under uncertainty based on statistical machine learning. Protection and Control of Modern Power Systems, 7(4), 1-27.
Zhang, B., Hu, W., Xu, X., Zhang, Z., & Chen, Z. (2023). Hybrid data-driven method for low-carbon economic energy management strategy in electricity-gas coupled energy systems based on transformer network and deep reinforcement learning. Energy, 273, 127183.
Li, X., Zhang, D., Zheng, Y., Hong, W., Wang, W., Xia, J., & Lv, Z. (2023). Evolutionary computation-based machine learning for smart city high-dimensional big data analytics. Applied Soft Computing, 133, 109955.
de Oliveira, R. A., & Bollen, M. H. (2023). Deep learning for power quality. Electric Power Systems Research, 214, 108887.
Oprea, S. V., Bâra, A., Puican, F. C., & Radu, I. C. (2021). Anomaly detection with machine learning algorithms and big data in electricity consumption. Sustainability, 13(19), 10963.
Devaraj, J., Madurai Elavarasan, R., Shafiullah, G. M., Jamal, T., & Khan, I. (2021). A holistic review on energy forecasting using big data and deep learning models. International journal of energy research, 45(9), 13489-13530.
Yoo, Y., Jo, H., & Ban, S. W. (2023). Lite and efficient deep learning model for bearing fault diagnosis using the CWRU dataset. Sensors, 23(6), 3157.
Hou, R., Kong, Y., Cai, B., & Liu, H. (2020). Unstructured big data analysis algorithm and simulation of Internet of Things based on machine learning. Neural Computing and Applications, 32(10), 5399-5407.
Ahmad, T., Madonski, R., Zhang, D., Huang, C., & Mujeeb, A. (2022). Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm. Renewable and Sustainable Energy Reviews, 160, 112128.
Kotsiopoulos, T., Sarigiannidis, P., Ioannidis, D., & Tzovaras, D. (2021). Machine learning and deep learning in smart manufacturing: The smart grid paradigm. Computer Science Review, 40, 100341.
Bendaouia, A., Qassimi, S., Boussetta, A., Benzakour, I., Benhayoun, A., Amar, O., et al. (2024). Hybrid features extraction for the online mineral grades determination in the flotation froth using Deep Learning. Engineering Applications of Artificial Intelligence, 129, 107680.
Liao, H., He, Y., Wu, X., Wu, Z., & Bausys, R. (2023). Reimagining multi-criterion decision making by data-driven methods based on machine learning: A literature review. Information Fusion, 101970.
Mishra, M., Nayak, J., Naik, B., & Abraham, A. (2020). Deep learning in electrical utility industry: A comprehensive review of a decade of research. Engineering Applications of Artificial Intelligence, 96, 104000.
Ullah, A., Javaid, N., Yahaya, A. S., Sultana, T., Al-Zahrani, F. A., & Zaman, F. (2021). A hybrid deep neural network for electricity theft detection using intelligent antenna-based smart meters. Wireless Communications and Mobile Computing, 2021, 1-19.
Adnan, A., Muhammed, A., Abd Ghani, A. A., Abdullah, A., & Hakim, F. (2021). An intrusion detection system for the internet of things based on machine learning: Review and challenges. Symmetry, 13(6), 1011.
Ma, X., Hu, H., & Ren, Y. (2023). A hybrid deep learning model based on feature capture of water level influencing factors and prediction error correction for water level prediction of cascade hydropower stations under multiple time scales. Journal of Hydrology, 617, 129044.
Wu, Z., Luo, G., Yang, Z., Guo, Y., Li, K., & Xue, Y. (2022). A comprehensive review on deep learning approaches in wind forecasting applications. CAAI Transactions on Intelligence Technology, 7(2), 129-143.
Sun, L., Liu, T., Xie, Y., Zhang, D., & Xia, X. (2021). Real-time power prediction approach for turbine using deep learning techniques. Energy, 233, 121130.
Faheem, M., & Al-Khasawneh, M. A. (2024). Multilayer Cyberattacks Identification and Classification Using Machine Learning in Internet of Blockchain (IoBC)-Based Energy Networks. Data in Brief, 2024, 110461.
Tursunboev, J., Palakonda, V., & Kang, J. M. (2024). Multi-Objective Evolutionary Hybrid Deep Learning for energy theft detection. Applied Energy, 363, 122847.
Javaid, N. (2021). A PLSTM, AlexNet and ESNN based ensemble learning model for detecting electricity theft in smart grids. IEEE Access, 9, 162935-162950.
Abou Houran, M., Bukhari, S. M. S., Zafar, M. H., Mansoor, M., & Chen, W. (2023). COA-CNN-LSTM: Coati optimization algorithm-based hybrid deep learning model for PV/wind power forecasting in smart grid applications. Applied Energy, 349, 121638.
Ma, L., & Sun, B. (2020). Machine learning and AI in marketing–Connecting computing power to human insights. International Journal of Research in Marketing, 37(3), 481-504.
Zhu, J., Dong, H., Zheng, W., Li, S., Huang, Y., & Xi, L. (2022). Review and prospect of data-driven techniques for load forecasting in integrated energy systems. Applied Energy, 321, 119269.
Tao, S., Liu, H., Sun, C., Ji, H., Ji, G., Han, Z., et al. (2023). Collaborative and privacy-preserving retired battery sorting for profitable direct recycling via federated machine learning. Nature Communications, 14(1), 8032.
Eren, Y., & Küçükdemiral, İ. (2024). A comprehensive review on deep learning approaches for short-term load forecasting. Renewable and Sustainable Energy Reviews, 189, 114031.
Chauhdary, S. H., Alkatheiri, M. S., Alqarni, M. A., & Saleem, S. (2023). An efficient evolutionary deep learning-based attack prediction in supply chain management systems. Computers and Electrical Engineering, 109, 108768.
DOI: https://doi.org/10.31449/inf.v49i25.8012
This work is licensed under a Creative Commons Attribution 3.0 License.