In response to the increasingly severe environmental pollution problem at present, many traditional power companies are facing the promotion of environmental protection work. The promotion of Energy Conservation and Emission Reduction (ECER) work needs sufficient theory and data support. Therefore, this study proposes a performance evaluation model for ECER in power enterprises based on the improved lion swarm algorithm optimized Least Squares Support Vector Machine (LSSVM). The evaluation index system has been reconstructed according to the current environment and era characteristics. The LSSVM has been used to evaluate the ECER work of power enterprises, and the training efficiency of Lion Swarm Optimization (LSO) algorithm has been solved using chaos theory. An improved LSO algorithm is utilized to improve the parameter selection problem of the LSSVM model, to achieve a scientific and objective evaluation of the ECER performance of power enterprises. The research findings denote that the algorithm put forward in this study has better training efficiency, higher accuracy and stability, as well as better response time. It also exhibits minimal errors in simulation experiments. In summary, the ECER performance evaluation model proposed by this research institute can effectively output correct scoring results, providing data support for the promotion of environmental protection work.

[1] Ali A. and Thakkar R., “Climate Changes through Data Science: Understanding and Mitigating Environmental Crisis,” Mesopotamian Journal of Big Data, vol. 2023, pp. 125-137, 2023. DOI:10.58496/MJBD/2023/017

[2] Bones I., Bos C., Moonen C., Hendrikse J., Stralen M., “Workflow for Automatic Renal Perfusion 864 The International Arab Journal of Information Technology, Vol. 21, No. 5, September 2024 Quantification Using ASL-MRI and Machine Learning,” Magnetic Resonance in Medicine, vol. 87, no. 2, pp. 800-809, 2022. DOI:10.1002/mrm.29016

[3] Chen J., Tan H., and Ma Y., “Distinguishing the Complex Effects of Foreign Direct Investment on Environmental Pollution: Evidence from China,” The Energy Journal, vol. 43, no. 4, pp. 27-44, 2022. https://doi.org/10.5547/01956574.43.4.jche

[4] Chen Y., Mei Y., Li M., Dang C., Huang L., Wu W., Wu Y., Yu X., Wang K., Gu L., Liu L., and Cao X., “Highly Selective CO2 Conversion to Methane or Syngas Tuned by CNTs@non-noble- metal cathodes in Zn-CO2 Flow Batteries,” Green Chemistry, vol. 23, no. 20, pp. 8138-8146, 2021. http://dx.doi.org/10.1039/D1GC02496E

[5] Daggash H. and Dowell N., “Delivering Low- Carbon Electricity Systems in Sub-Saharan Africa: Insights from Nigeria,” Energy and Environmental Science, vol. 14, no. 7, pp. 4018- 4037, 2021. DOI:10.1039/D1EE00746G

[6] Dubey A., Yadav P., and Patel S., “Usage of Statistical Techniques to Monitor the Performance of Wind Turbines,” The International Arab Journal of Information Technology, vol. 21, no. 2, pp. 338-349, 2024. DOI: 10.34028/iajit/21/2/15

[7] Huang M., Zhou C., Wen R., Tian J., Huang W., Wei H., and Lu J., “Membraneless Photocatalytic Fuel Cell with Double Photoelectrodes for Simultaneous Electricity Generation and Pollutant Degradation,” Journal of the Electrochemical Society, vol. 169, no. 2, pp. 1-10, 2022. DOI:10.1149/1945-7111/ac4b21

[8] Ishaq H. and Dincer I., “Investigation of a New Energy System for Clean Methanol Production,” International Journal of Energy Research, vol. 45, no. 12, pp. 17109-17119, 2021. DOI:10.1002/er.5369

[9] Jame H., Sarker S., Islam M., Rauf A., Ahsan S., Nishat S., Jani M., Shorowordi K., Carbonara J., and Ahmed S., “Supervised Machine Learning- Aided SCAPS-based Quantitative Analysis for the Discovery of Optimum Bromine Doping in Methylammonium Tin-based Perovskite (MASnl3-xBrx),” ACS Applied Materials and Interfaces, vol. 14, no. 1, pp. 502-516, 2022. DOI:10.1021/acsami.1c15030

[10] Liu P., Ahmad S., Abdullah S., Mohammed M., and Al‐Shomrani M., “A New Approach to Three‐ Way Decisions Making Based on Fractional Fuzzy Decision‐Theoretical Rough Set,” International Journal of Intelligent Systems, vol. 37, no. 3, pp. 2438-2457, 2021. https://doi.org/10.1002/int.22779

[11] Lu J., “Can the Central Environmental Protection Inspection Reduce Transboundary Pollution? Evidence from River Water Quality Data in China,” Journal of Cleaner Production, vol. 332, no. 2, pp. 1-10, 2022. DOI:10.1016/j.jclepro.2021.130030

[12] Ma Q., Wang S., Zhou Q., and Gan X., “Parallel Optimization of Design and Manufacture for Carbon Fiber Reinforced Plastic Oil Pan Based on the Thickness Distribution,” Polymer Composites, vol. 42, no. 12, pp. 6648-6663, 2021. https://doi.org/10.1002/pc.26329

[13] Munonye K. and Peter M., “Machine Learning Approach to Vulnerability Detection in OAuth 2.0 Authentication and Authorization Flow,” International Journal of Information Security, vol. 21, no. 2, pp. 223-237, 2022. https://doi.org/10.1007/s10207-021-00551-w

[14] Pandit N., Roy D., Mandal S., and Pathak B., “Rational Designing of Bimetallic/Trimetallic Hydrogen Evolution Reaction Catalysts Using Supervised Machine Learning,” The Journal of Physical Chemistry Letters, vol. 13, no. 32, pp. 7583-7593, 2022. DOI:10.1021/acs.jpclett.2c01401

[15] Perez-Canedo B. and Verdegay J., “On the Application of a Lexicographic Method to Fuzzy Linear Programming Problems,” Journal of Computational and Cognitive Engineering, vol. 2, no. 1, pp. 47-56, 2023. https://doi.org/10.47852/bonviewJCCE20235142025

[16] Sánchez A., Castellano E., Martín M., and Vega P., “Evaluating Ammonia as Green Fuel for Power Generation: A Thermo-Chemical Perspective,” Applied Energy, vol. 293, no. 9, pp. 1-12, 2021. https://doi.org/10.1016/j.apenergy.2021.116956

[17] Sebastian S., Hannes G., Tremmelc S., and Wartzacka S., “Prediction of Rolling Bearing Cage Dynamics Using Dynamic Simulations and Machine Learning Algorithms,” Tribology Transactions, vol. 65, no. 2, pp. 225-241, 2021. https://doi.org/10.1080/10402004.2021.1934618

[18] Sun Y., Sun Y., and Li X., “Constructing a Green Financial Innovation System with the PPP Environmental Protection Industry Fund,” International Journal of Technology Management, vol. 85, no. 2/4, pp. 319-332, 2021. DOI:10.1504/IJTM.2021.115272

[19] Tripathi A., Srivastava S., Mittal H., Sinhua S., and Yadav V., “Multi-Objective ANT Lion Optimization Algorithm Based Mutant Test Case Selection for Regression Testing,” Journal of Scientific and Industrial Research, vol. 80, no. 7, pp. 582-592, 2021. https://nopr.niscpr.res.in/bitstream/123456789/57 976/1/JSIR%2080%287%29%20582-592.pdf

[20] Vecchi E., Pospisil L., Albrecht S., O’Kane T., and Horenko I., “eSPA+: Scalable Entropy- Optimal Machine Learning Classification for Small Data Problems,” Neural Computation, vol. 34, no. 5, pp. 1220-1255, 2022. DOI:10.1162/neco_a_01490 Evaluation of Emission Reduction Performance of Power Enterprises Based on Least ... 865

[21] Yan S., Xu W., He H., Shen J., Shi Y., Xu S., Liu B., Ma Y., Xiao Q., Zhang Z., Hu M., Liang J., and Ren X., “Efficient Synthesis of C4 Compound with Low Carbon Emission from Acetaldehyde: Aldol Condensation Catalyzed by Regulable Acidic-Alkaline Al/Mg Sites of CuMgAlO,” Industrial and Engineering Chemistry Research, vol. 61, no. 27, pp. 9544-9551, 2022. https://doi.org/10.1021/acs.iecr.2c00456