Authors:
Basim Galeb,Haider Saad,Haitham Bashar,Kadhum Al-Majdi,Aqeel Al-Hilali,DOI NO:
https://doi.org/10.26782/jmcms.2024.05.00008Keywords:
Anomaly Detection System,Abnormal Consumption,Energy-saving Systems,Statistical Modelling Techniques,Time Series Forecasting.,Abstract
Over the last several years, there has been a significant increase in the amount of focus placed on the infrastructure development of smart cities. The primary issue that academics are attempting to address is the issue of energy efficiency. One of these issues was the identification of anomalies in energy usage, which was an essential component that needed to be taken into consideration when managing energy-saving systems that were efficient, hence lowering the total energy consumption and carbon emissions. Therefore, the proposal of a strong approach that is based on the Internet of Things (IoT) might provide more relevance for the identification of abnormal consumption in buildings and the provision of this information to customers and governments so that it can be handled in an appropriate manner to minimize payments. Consequently, the purpose of this work is to explore three different optimization methods, namely ADAM, AadMax, and Nadam, and to advocate for an optimization approach that makes use of the LSTM algorithm to identify anomalies. Statistical modelling techniques such as ARIMA and SARIMAX are used for the purpose of time series forecasting. The findings of the anomaly detection system reveal that the best results are obtained by using LSTM in conjunction with Nadar. The MSE and RMSE values reached were 0.15348 and 0.02356 respectively. Additionally, the ARIMA model yields the best overall results, with the AIC value being 0.13859 and the MSE value being 300.94365 correspondingly. Confirmation of the suggested model's dependability and flexibility in optimizing anomaly detection is provided by this particular fact.Refference:
I. Abdulwahid, M. M., Al-Ani, O. A. S., Mosleh, M. F., & Abd-Alhameed, R. A. : ‘Investigation of millimeter-wave indoor propagation at different frequencies’. In 2019 4th Scientific International Conference Najaf (SICN). (2019, April). (pp. 25-30). IEEE.
II. Abdulwahid, M. M., Al-Ani, O. A. S., Mosleh, M. F., & Abd-Alhameed, R. A. : ‘A Comparison between Different C-band and mmWave band Frequencies for Indoor Communication’. J. Commun., 14(10), (2019). pp. 892-899.
III. Abdulwahid, M. M., Al-Hakeem, M. S., Mosleh, M. F., & Abd-alhmeed, R. A. : ‘Investigation and optimization method for wireless AP deployment based indoor network’. In IOP Conference Series: Materials Science and Engineering. Vol. 745, No. 1. (2020, February). pp. 012031. IOP Publishing.
IV. Abdulwahid, M. M., & Kurnaz, S. : ‘The channel WDM system incorporates of Optical Wireless Communication (OWC) hybrid MDM-PDM for higher capacity (LEO-GEO) inter-satellite link’. Optik. VoL. 273 (2023). 170449. doi.org/10.1016/j.ijleo.2022.170449
V. Abd-Alhameed, R. A., Abdulwahid, M. M., & Mosleh, M. F. : ‘Effects of Antenna Directivity and Polarization on Indoor Multipath Propagation Characteristics for different mmWave frequencies’. Informatica 2(1). 2021. pp. 20-28.
VI. Ali, O. M. A., Kareem, S. W., & Mohammed, A. S. : ‘Evaluation of Electrocardiogram Signals Classification Using CNN, SVM, and LSTM Algorithm: A review’. In 2022 8th International Engineering Conference on Sustainable Technology and Development (IEC), pp. 185-191. IEEE, 2022.
VII. Almetwali, A. S., Bayat, O., Abdulwahid, M. M., & Mohamadwasel, N. B. : ‘Design and Analysis of 50 Channel by 40 Gbps DWDM-RoF System for 5G Communication Based on Fronthaul Scenario’. In Proceedings of Third Doctoral Symposium on Computational Intelligence. (2023). (pp. 109-122). Springer, Singapore.
VIII. Alhamadani, N. B., & Abdelwahid, M. M. : ‘Implementation of microstrip patch antenna using MATLAB. Informatica’. Journal of Applied Machines Electrical Electronics Computer Science and Communication Systems. 2(1). (2021). pp. 29-35.
IX. Alsalemi A, Himeur Y, Bensaali F, et al. : ‘Achieving domestic energy efficiency using micro‐moments and intelligent recommendations. IEEE Access. Vol.8. 2020. pp.15047‐15055,
X. Ampountolas, A. : ‘Modeling and Forecasting Daily Hotel Demand: A Comparison Based on SARIMAX, Neural Networks, and GARCH Models’. Forecasting, Vol.3, No. 3, pp.580-595., 2021
XI. A. Mosavi, A. Bahmani. : ‘Energy consumption prediction using machine learning: A review’. Preprints 2019, doi.org/10.20944/preprints201903.0131.v1, 2019.
XII. Alsalemi A, Himeur Y, Bensaali F, et al. : ‘Achieving domestic energy efficiency using micro‐moments and intelligent recommendations. IEEE Access. Vol.8. 2020. pp.15047‐15055.
XIII. Box, G. E., and Jenkins, G. M. : ‘Time series analysis: forecasting and control’. Holden dsy. Inc. California, 1976.
XIV. Burhan, I. M., Al-Hakeem, M. S., Abdulwahid, M. M., & Mosleh, M. F. : ‘Investigating the Access Point height for an indoor IOT services’. In IOP Conference Series: Materials Science and Engineering. Vol. 881, No. 1, (2020, July). pp. 012116. IOP Publishing.
XV. Buzau, M. M., Tejedor-Aguilera, J., et al. : ‘Hybrid deep neural networks for detection of non-technical losses in electricity smart meters. IEEE Transactions on Power Systems, Vol. 35, No. 2, 2019. pp.1254-1263.
XVI. Chou, J. S., & Telaga, A. S. : ‘Real-time detection of anomalous power consumption’. Renewable and Sustainable Energy Reviews, Vol. 33. 2014. pp.400-411.,
XVII. Farsi, M., Hosahalli, D., Manjunatha, B. R., Gad, I., et al. : ‘Parallel genetic algorithms for optimizing the SARIMA model for better forecasting of the NCDC weather data. Alexandria Engineering Journal, Vol.60, No. 1, (2021). pp. 1299-1316.
XVIII. Feng, L., Xu, S., Zhang, L., Wu, J., Zhang, J., et al. : ‘Anomaly detection for electricity consumption in cloud computing: framework, methods, applications, and challenges’. EURASIP Journal on Wireless Communications and Networking, Vol. 1. (2020). pp.1-12,
XIX. F. Abayaje, S. A. Hashem, H. S. Obaid, Y. S. Mezaal, and S. K. Khaleel. : ‘A miniaturization of the UWB monopole antenna for wireless baseband transmission’. Periodicals of Engineering and Natural Sciences, vol. 8. no. 1. (2020). pp. 256-262.
XX. G. Zhao, L. Xing. ‘Reliability analysis of IoT systems with competitions from cascading probabilistic function dependence. Reliab. Eng. Syst. Saf., Vol. 198. (2020). pp.106812. https://doi.org/10.1016/j.ress.2020.106812
XXI. Himeur Y, Elsalemi A, Bensaali F, Amira A. : ‘Robust event‐based non‐intrusive appliance recognition using multi‐scale wavelet packet tree and ensemble bagging tree. Appl Energy. Val. 267. (2020). pp.114877.
XXII. Himeur, Y., Alsalemi, A., Bensaali, et al. : ‘A novel approach for detecting anomalous energy consumption based on micro-moments and deep neural networks’. Cognitive Computation, Vol. 12. No. 6. (2020). pp. 1381-1401.
XXIII. Himeur, Y., Alsalemi, A., Bensaali, F., at al. : ‘Smart power consumption abnormality detection in buildings using micromoments and improved K‐nearest neighbors. International Journal of Intelligent Systems, Vol. 36, No. 6. (2012). pp. 2865-2894.
XXIV. Hyndman, R. J., and Athanasopoulos, G. : ‘Forecasting: principles and practice. OTexts. 2018.
XXV. H. A. Hussein, Y. S. Mezaal, and B. M. Alameri. : ‘Miniaturized microstrip diplexer based on FR4 substrate for wireless communications’. Elektron. Ir Elektrotech. Vol. 27 No. 5. (2021) . doi.org/10.5755/j02.eie.28942
XXVI. I. O. Essiet, Y. Sun, Z. Wang. ‘Optimized energy consumption model for smart home using improved differential evolution algorithm’. Energy Vol. 172. (2019). pp.354–365. https://doi.org/10.1016/j.
XXVII. Jamal, S. A., Ibrahim, A. A., Abdulwahid, M. M., & Wasel, N. B. M. (2020). ‘Design and implementation of multilevel security-based home management system’. International Journal of Advanced Trends in Computer Science and Engineering. Vol. 9(4). (2020). pp. 5716-5720. doi.org/10.30534/ijatcse/2020/224942020
XXVIII. J. Ali and Y. Miz’el. ‘A new miniature Peano fractal-based bandpass filter design with 2nd harmonic suppression 3rd IEEE International Symposium on Microwave.’ Antenna, Propagation and EMC Technologies for Wireless Communications, Beijing, China, 2009.
XXIX. Lin, G., & Claridge, D. E. : ‘A temperature-based approach to detect abnormal building energy consumption’. Energy and Buildings, Vol. 93. (2015). pp. 110-118.
XXX. Liu, J., Shahroudy, A., Xu, D., et al. : ‘Spatio-temporal lstm with trust gates for 3d human action recognition’. In European conference on computer vision. (2016). pp. 816-833. Springer, Cham.,
XXXI. Liu Y, Geng G, Gao S, Xu W. : Non‐intrusive energy use monitoring for a group of electrical appliances’. IEEE Trans Smart Grid, Vol.9, No. 4. (2018). pp. 3801‐3810,
XXXII. Luo, Y., Li, W., & Qiu, S. : ‘Anomaly detection based latency-aware energy consumption optimization for IoT data-flow services’. Sensors, Vol.20. No. 1. (2019). pp.122.
XXXIII. Malki, A., Atlam, E. S., and Gad, I. : ‘Machine learning approach of detecting anomalies and forecasting time-series of IoT devices’. Alexandria Engineering Journal, Vol. 61 No. 11. (2022). pp. 8973-8986.
XXXIV. Mohsen, D. E., Abbas, E. M., & Abdulwahid, M. M. : ‘Design and Implementation of DWDM-FSO system for Tbps data rates with different atmospheric Attenuation’. In 2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA) (2022, June). (pp. 1-7). IEEE. 10.1109/HORA55278.2022.9799974
XXXV. Mohsen, D. E., Abbas, E. M., & Abdulwahid, M. M. : ‘Performance Analysis of OWC System based (S-2-S) Connection with Different Modulation Encoding’. International Journal of Intelligent Systems and Applications in Engineering. 11(4s). (2023). pp. 400-408.
XXXVI. Monner, D., and Reggia, J. A. : ‘A generalized LSTM-like training algorithm for second-order recurrent neural networks. Neural Networks, Vol. 25. (2012). pp.70-83.
XXXVII. Mustapha, A., Mohamed, L., and Ali, K. : ‘Comparative study of optimization techniques in deep learning: Application in the ophthalmology field’. In Journal of Physics: Conference Series, Vol. 1743, No. 1. (2021) pp. 012002. IOP Publishing.
XXXVIII. M. Kh. : ‘Cybercrime Challenges in Iraqi Academia: Creating Digital Awareness for Preventing Cybercrimes.’ International Journal of Cyber Criminology. vol. 16, no. 2. (2022). pp. 1–14.
XXXIX. M. Q. Mohammed. : ‘HARNESSING CLOUD OF THING AND FOG COMPUTING IN IRAQ: ADMINISTRATIVE INFORMATICS SUSTAINABILITY’. Journal of Mechanics of Continua and Mathematical Sciences. vol. 19, no. 2. (2024) pp. 66–78. 10.26782/jmcms.2024.02.00004
XL. M. S. Jameel, Y. S. Mezaal, and D. C. Atilla. : ‘Miniaturized coplanar waveguide-fed UWB Antenna for wireless applications’. Symmetry. vol. 15, no. 3. (2023). pp. 633. doi.org/10.3390/sym15030633
XLI. M. S. Shareef et al. : ‘Cloud of Things and fog computing in Iraq: Potential applications and sustainability.’ : Heritage and Sustainable Development. vol. 5, no. 2. (2023). pp. 339–350.
XLII. M. Zekic´-Susˇac, S. Mitrovic´, A. Has. : ‘Machine learning based system for managing energy efficiency of public sector as an approach towards smart cities’. Int. J. Inf. Manage. Vol 58. (2021) doi.org/10.1016/j.ijinfomgt.2020.102074
XLIII. Sardianos C, Varlamis I, Chronis C, et al. : ‘A model for predicting room occupancy based on motion sensor data’. In: 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT). (2020). pp. 394‐399.
XLIV. Sardianos C, Varlamis I, Dimitrakopoulos G, et al. : ‘REHAB‐C: recommendations for energy HABits change’. Future Gener Comput Syst. Vol. 112. (2020). pp. 394‐407. 10.1016/j.future.2020.05.041
XLV. Sohani, A., Sayyaadi, H., Cornaro, C., Shahverdian, et. al. : ‘Using machine learning in photovoltaics to create smarter and cleaner energy generation systems: A comprehensive review’. Journal of Cleaner Production. Vol 364 (2022). doi.org/10.1016/j.jclepro.2022.132701
XLVI. S. A. Abdulameer. : ‘Security Readiness in Iraq: Role of the Human Rights Activists’. International Journal of Cyber Criminology. vol. 16. no. 2. (2022) pp. 1–14.
XLVII. S. I. Yahya et al.. ‘A New Design Method for Class-E Power Amplifiers Using Artificial Intelligence Modeling for Wireless Power Transfer Applications’. Electronics. vol. 11, no. 21 (2022). pp. 3608.
XLVIII. S. Roshani et al. : ‘Design of a compact quad-channel microstrip diplexer for L and S band applications’. Micromachines (Basel), vol. 14, no. 3. (2023). doi.org/10.3390/mi14030553
XLIX. T. Abd, Y. S. Mezaal, M. S. Shareef, S. K. Khaleel, H. H. Madhi, and S. F. Abdulkareem. : ‘Iraqi e-government and cloud computing development based on unified citizen identification’. Periodicals of Engineering and Natural Sciences. vol. 7. no. 4. (2019) pp. 1776–1793.
L. Vagropoulos, S. I., Chouliaras, G. I., Kardakos, E. G., et al. : ‘Comparison of SARIMAX, SARIMA, modified SARIMA and ANN-based models for short-term PV generation forecasting’. In 2016 IEEE International Energy Conference (ENERGYCON), pp. 1-6. IEEE. 10.1109/ENERGYCON.2016.7514029
LI. V. Marinakis, H. Doukas. : ‘An advanced IoT-based system for intelligent energy management in buildings’. Sensors, Vol.18, No. 2, (2018). pp.610. https://doi.org/10.3390/s18020610
LII. Wu, D., Zhang, L., and Lin, L. : ‘Based on the moving average and target motion information for detection of weak small target’. In 2018 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS), pp. 641-644. IEEE, 2018.
LIII. Yang, L., & Yang, H. : ‘A Combined ARIMA-PPR Model for Short-Term Load Forecasting’. In 2019 IEEE Innovative Smart Grid Technologies-Asia (ISGT Asia). IEEE. (2019). pp. 3363-3367.
LIV. Y. S. Mezaal and H. T. Eyyuboglu. : ‘A new narrow band dual-mode microstrip slotted patch bandpass filter design based on fractal geometry’. In 2012 7th International Conference on Computing and Convergence Technology (ICCCT). IEEE. (2012). pp. 1180-1184.
LV. Y. S. Mezaal, and J. K. Ali. : ‘A new design of dual band microstrip bandpass filter based on Peano fractal geometry: Design and simulation results’. Presented at the 2013 13th Mediterranean Microwave Symposium (MMS), IEEE. 2013. 10.1109/MMS.2013.6663140
LVI. Y. S. Mezaal and S. F. Abdulkareem. : ‘New microstrip antenna based on quasi-fractal geometry for recent wireless systems’. In 2018 26th Signal Processing and Communications Applications Conference (SIU). IEEE. (2018) pp. 1-4.
LVII. Y. S. Mezaal et al. : ‘Cloud computing investigation for cloud computer networks using cloudanalyst’. Journal of Theoretical and Applied Information Technology, vol. 96. no. 20. (2018)
LVIII. Y.S. Mezaal et al. : ‘Cloud computing investigation for cloud computer networks using cloudanalyst’. Journal of Theoretical and Applied Information Technology. vol. 96, no. 20, (2018)
LIX. Y. S. Mezaal, H. H. Saleh, and H. Al-Saedi. ‘New compact microstrip filters based on quasi fractal resonator’. Advanced Electromagnetics. vol. 7. no. 4. (2018) pp. 93-102.
LX. Y. S.Mezaal, Eyyuboglu, H. T., &Ali, J. K. Wide bandpass and narrow bandstop microstrip filters based onHilbert fractal geometry: design and simulation results. PloS one, 9(12),e115412, 2014.
LXI. Zaal, R. M., Mustafa, F. M., Abbas, E. I., Mosleh, M. F., & Abdulwahid, M. M. : ‘Real measurement of optimal access point localizations’. In IOP Conference Series: Materials Science and Engineering. Vol. 881. No. 1. (2020). pp. 012119). IOP Publishing.