ENERGY FORECASTING MODELS FOR POWER AND TELECOMMUNICATION INFRASTRUCTURES: A SYSTEMATIC REVIEW
DOI:
https://doi.org/10.55197/qjoest.v6i2.213Keywords:
energy, infrastructures, machine-learning, forecasting, deep-learning, telecommunicationsAbstract
Energy demand for power and telecommunication infrastructures has risen in recent years owing to technological advancements. The downtimes caused by an inadequate energy supply to critical infrastructure pose a great risk to daily activities. Hence, knowledge of future energy demands is pertinent to minimizing losses and operational costs, while ensuring consistent and reliable services. This article comprehensively reviews different models for predicting energy requirements by power and telecommunication infrastructure. The findings reveal that, while traditional models such as linear regression are simple to implement, models utilizing machine learning (ML) and deep learning (DL) techniques demonstrate superior performance in predicting energy consumption, yielding more precise outcomes. It has also shown that ML and DL models, including long short-term memory (LSTM), convolutional neural networks (CNN), Gated Recurrent Units (GRU), and hybrid architectures, are particularly effective for handling the complexities of long-term forecasting and adaptive systems. Thus, this current study offers valuable insights for academia, researchers, and energy personnel in network planning of the power and telecommunication industries to improve energy efficiency and cost management by analyzing historical data, identifying complex patterns, enabling real-time adaptations, and accurately forecasting their energy requirements. Researchers can also build upon the identified gaps to enhance the existing models and improve productivity.
References
[1] Abumohsen, M., Owda, A.Y., Owda, M. (2023): Electrical load forecasting using LSTM, GRU, and RNN algorithms. – Energies 16(5): 31p.
[2] Aguilar Madrid, E., Antonio, N. (2021): Short-term electricity load forecasting with machine learning. – Information 12(2): 21p.
[3] 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: 35p.
[4] Ahmad, T., Zhang, D., Huang, C., Zhang, H., Dai, N., Song, Y., Chen, H. (2021): Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities. – Journal of Cleaner Production 289: 31p.
[5] Ahmadi, A., Nabipour, M., Taheri, S., Mohammadi-Ivatloo, B., Vahidinasab, V. (2022): A new false data injection attack detection model for cyberattack resilient energy forecasting. – IEEE Transactions on Industrial Informatics 19(1): 371-381.
[6] Alaraj, M., Kumar, A., Alsaidan, I., Rizwan, M., Jamil, M. (2021): Energy production forecasting from solar photovoltaic plants based on meteorological parameters for qassim region, Saudi Arabia. – IEEE Access 9: 83241-83251.
[7] Alhendi, A., Al-Sumaiti, A.S., Marzband, M., Kumar, R., Diab, A.A.Z. (2023): Short-term load and price forecasting using artificial neural network with enhanced Markov chain for ISO New England. – Energy Reports 9: 4799-4815.
[8] AlShafeey, M., Csaki, C. (2024): Adaptive machine learning for forecasting in wind energy: A dynamic, multi-algorithmic approach for short and long-term predictions. – Heliyon 10(15): 18p.
[9] Alsharef, A., Aggarwal, K., Sonia, Kumar, M., Mishra, A. (2022): Review of ML and AutoML solutions to forecast time-series data. – Archives of Computational Methods in Engineering 29(7): 5297-5311.
[10] Altunkaya, D., Yılmaz, B. (2020): Multivariate Short-term Load Forecasting Using Deep Learning Algorithms. – The Eurasia Proceedings of Science Technology Engineering and Mathematics 11: 14-19.
[11] Amasyali, K., El-Gohary, N.M. (2018): A review of data-driven building energy consumption prediction studies. – Renewable and Sustainable Energy Reviews 81: 1192-1205.
[12] Andriopoulos, N., Magklaras, A., Birbas, A., Papalexopoulos, A., Valouxis, C., Daskalaki, S., Birbas, M., Housos, E., Papaioannou, G.P. (2020): Short term electric load forecasting based on data transformation and statistical machine learning. – Applied Sciences 11(1): 22p.
[13] Arruda, H.F.D., Benatti, A., Comin, C.H., Costa, L.D.F. (2022): Learning deep learning. – Revista Brasileira de Ensino de Física 44: 11p.
[14] Arsene, C., Parisio, A. (2024): Deep convolutional neural networks for short-term multi-energy demand prediction of integrated energy systems. – International Journal of Electrical Power & Energy Systems 160: 19p.
[15] Aslam, S., Herodotou, H., Mohsin, S.M., Javaid, N., Ashraf, N., Aslam, S. (2021): A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids. – Renewable and Sustainable Energy Reviews 144: 23p.
[16] Aurangzeb, K., Alhussein, M., Javaid, K., Haider, S.I. (2021): A pyramid-CNN based deep learning model for power load forecasting of similar-profile energy customers based on clustering. – IEEE Access 9: 14992-15003.
[17] Aurna, N.F., Rubel, M.T.M., Siddiqui, T.A., Karim, T., Saika, S., Arifeen, M.M., Mahbub, T.N., Reza, S.S., Kabir, H. (2021): Time series analysis of electric energy consumption using autoregressive integrated moving average model and Holt Winters model. – TELKOMNIKA (Telecommunication Computing Electronics and Control) 19(3): 991-1000.
[18] Baba, A. (2022): Electricity-consuming forecasting by using a self-tuned ANN-based adaptable predictor. – Electric Power Systems Research 210: 108134.
[19] Barak, S., Sadegh, S.S. (2016): Forecasting energy consumption using ensemble ARIMA-ANFIS hybrid algorithm. – International Journal of Electrical Power & Energy Systems 82: 92-104.
[20] Bloomfield, H.C., Brayshaw, D.J., Deakin, M., Greenwood, D. (2022): Hourly historical and near-future weather and climate variables for energy system modelling. – Earth System Science Data 14(6): 2749-2766.
[21] Bousdekis, A., Lepenioti, K., Apostolou, D., Mentzas, G. (2021): A review of data-driven decision-making methods for industry 4.0 maintenance applications. – Electronics 10(7): 20p.
[22] Chen, Z., Xiao, F., Guo, F., Yan, J. (2023): Interpretable machine learning for building energy management: A state-of-the-art review. – Advances in Applied Energy 9: 19p.
[23] Chévez, P., Martini, I. (2024): Applying neural networks for short and long-term hourly electricity consumption forecasting in universities: A simultaneous approach for energy management. – Journal of Building Engineering 97: 23p.
[24] Chinnaraji, R., Ragupathy, P. (2022): Accurate electricity consumption prediction using enhanced long short‐term memory. – IET Communications 16(8): 830-844.
[25] Ciulla, G., D'Amico, A. (2019): Building energy performance forecasting: A multiple linear regression approach. – Applied Energy 253: 16p.
[26] Clements, A.E., Hurn, A.S., Li, Z. (2016): Forecasting day-ahead electricity load using a multiple equation time series approach. – European Journal of Operational Research 251(2): 522-530.
[27] Dalal, A.A., AlRassas, A.M., Al-qaness, M.A., Cai, Z., Aseeri, A.O., Abd Elaziz, M., Ewees, A.A. (2023): TLIA: Time-series forecasting model using long short-term memory integrated with artificial neural networks for volatile energy markets. – Applied Energy 343: 14p.
[28] De Oliveira, E.M., Oliveira, F.L.C. (2018): Forecasting mid-long term electric energy consumption through bagging ARIMA and exponential smoothing methods. – Energy 144: 776-788.
[29] Del Real, A.J., Dorado, F., Durán, J. (2020): Energy demand forecasting using deep learning: applications for the French grid. – Energies 13(9): 15p.
[30] Dinesh, C., Makonin, S., Bajić, I.V. (2019): Residential power forecasting using load identification and graph spectral clustering. – IEEE Transactions on Circuits and Systems II: Express Briefs 66(11): 1900-1904.
[31] Dudek, G. (2016): Pattern-based local linear regression models for short-term load forecasting. – Electric Power Systems Research 130: 139-147.
[32] El Maghraoui, A., Ledmaoui, Y., Laayati, O., El Hadraoui, H., Chebak, A. (2022): Smart energy management: A comparative study of energy consumption forecasting algorithms for an experimental open-pit mine. – Energies 15(13): 22p.
[33] Falkenberg, R., Sliwa, B., Piatkowski, N., Wietfeld, C. (2018): Machine learning based uplink transmission power prediction for LTE and upcoming 5G networks using passive downlink indicators. – In 2018 IEEE 88th Vehicular Technology Conference (VTC-Fall), IEEE 7p.
[34] Fallah, S.N., Deo, R.C., Shojafar, M., Conti, M., Shamshirband, S. (2018): Computational intelligence approaches for energy load forecasting in smart energy management grids: state of the art, future challenges, and research directions. – Energies 11(3): 31p.
[35] Farsi, B., Amayri, M., Bouguila, N., Eicker, U. (2021): On short-term load forecasting using machine learning techniques and a novel parallel deep LSTM-CNN approach. – IEEE Access 9: 31191-31212.
[36] Forootan, M.M., Larki, I., Zahedi, R., Ahmadi, A. (2022): Machine learning and deep learning in energy systems: A review. – Sustainability 14(8): 49p.
[37] Granderson, J., Fernandes, S., Crowe, E., Sharma, M., Jump, D., Johnson, D. (2023): Accuracy of hourly energy predictions for demand flexibility applications. – Energy and Buildings 295: 10p.
[38] Gürses-Tran, G., Flamme, H., Monti, A. (2020): Probabilistic load forecasting for day-ahead congestion mitigation. – In 2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), IEEE 6p.
[39] Huang, J., Pang, C., Yang, W., Zeng, X., Zhang, J., Huang, C. (2022): A deep learning neural network for the residential energy consumption prediction. – IEEJ Transactions on Electrical and Electronic Engineering 17(4): 575-582.
[40] Huang, Q., Li, J., Zhu, M. (2020): An improved convolutional neural network with load range discretization for probabilistic load forecasting. – Energy 203: 14p.
[41] IEA, P. (2022): World energy outlook 2022. – Paris, France: International Energy Agency (IEA) 524p.
[42] Impram, S., Nese, S.V., Oral, B. (2020): Challenges of renewable energy penetration on power system flexibility: A survey. – Energy Strategy Reviews 31: 12p.
[43] Jahangir, H., Golkar, M.A., Alhameli, F., Mazouz, A., Ahmadian, A., Elkamel, A. (2020): Short-term wind speed forecasting framework based on stacked denoising auto-encoders with rough ANN. – Sustainable Energy Technologies and Assessments 38: 19p.
[44] Jha, S.K., Bilalovic, J., Jha, A., Patel, N., Zhang, H. (2017): Renewable energy: Present research and future scope of Artificial Intelligence. – Renewable and Sustainable Energy Reviews 77: 297-317.
[45] Ke, K., Hongbin, S., Chengkang, Z., Brown, C. (2019): Short-term electrical load forecasting method based on stacked auto-encoding and GRU neural network. – Evolutionary Intelligence 12: 385-394.
[46] Khan, A.N., Iqbal, N., Ahmad, R., Kim, D.H. (2021): Ensemble prediction approach based on learning to statistical model for efficient building energy consumption management. – Symmetry 13(3): 26p.
[47] Khwaja, A.S., Anpalagan, A., Naeem, M., Venkatesh, B. (2020): Joint bagged-boosted artificial neural networks: Using ensemble machine learning to improve short-term electricity load forecasting. – Electric Power Systems Research 179: 7p.
[48] Kim, T., Lee, D., Hwangbo, S. (2024): A deep-learning framework for forecasting renewable demands using variational auto-encoder and bidirectional long short-term memory. – Sustainable Energy, Grids and Networks 38: 15p.
[49] Kiranyaz, S., Avci, O., Abdeljaber, O., Ince, T., Gabbouj, M., Inman, D.J. (2021): 1D convolutional neural networks and applications: A survey. – Mechanical Systems and Signal Processing 151: 21p.
[50] Kumar, C.M.S., Singh, S., Gupta, M.K., Nimdeo, Y.M., Raushan, R., Deorankar, A.V., Kumar, T.A., Rout, P.K., Chanotiya, C.S., Pakhale, V.D., Nannaware, A.D. (2023): Solar energy: A promising renewable source for meeting energy demand in Indian agriculture applications. – Sustainable Energy Technologies and Assessments 55: 17p.
[51] Laayati, O., Bouzi, M., Chebak, A. (2022): Smart energy management system: design of a monitoring and peak load forecasting system for an experimental open-pit mine. – Applied System Innovation 5(1): 18p.
[52] Lee, J., Cho, Y. (2022): National-scale electricity peak load forecasting: Traditional, machine learning, or hybrid model? – Energy 239: 16p.
[53] Li, D., Xiao, C., Zeng, X., Shi, Q. (2022): Short-mid term electricity consumption prediction using non-intrusive attention-augmented deep learning model. – Energy Reports 8: 10570-10581.
[54] Liu, H., Zhang, X., Shen, X., Sun, H. (2021): A federated learning framework for smart grids: Securing power traces in collaborative learning. – ArXiv Preprint 9p.
[55] Liu, P., Zheng, P., Chen, Z. (2019): Deep learning with stacked denoising auto-encoder for short-term electric load forecasting. – Energies 12(12): 15p.
[56] Lv, P., Liu, S., Yu, W., Zheng, S., Lv, J. (2020): EGA-STLF: A hybrid short-term load forecasting model. – IEEE Access 8: 31742-31752.
[57] Mocanu, E., Nguyen, P.H., Gibescu, M., Kling, W.L. (2016): Deep learning for estimating building energy consumption. – Sustainable Energy, Grids and Networks 6: 91-99.
[58] Mohanty, S., Patra, P.K., Sahoo, S.S., Mohanty, A. (2017): Forecasting of solar energy with application for a growing economy like India: Survey and implication. – Renewable and Sustainable Energy Reviews 78: 539-553.
[59] Nti, I.K., Teimeh, M., Nyarko-Boateng, O., Adekoya, A.F. (2020): Electricity load forecasting: a systematic review. – Journal of Electrical Systems and Information Technology 7: 1-19.
[60] Obinna, I., Osawaru, O. (2020): Modelling of power consumption in two base stations, using ugbor station and benson idahosa university station in benin city as a case study. – International Journal of Innovative Science and Research Technology 5(6): 1153-1154.
[61] Peng, W., Xu, L., Li, C., Xie, X., Zhang, G. (2019): Stacked autoencoders and extreme learning machine based hybrid model for electrical load prediction. – Journal of Intelligent & Fuzzy Systems 37(4): 5403-5416.
[62] Qi, J., Zhou, Q., Lei, L., Zheng, K. (2021): Federated reinforcement learning: Techniques, applications, and open challenges. – ArXiv Preprint 39p.
[63] Rafi, S.H., Deeba, S.R., Hossain, E. (2021): A short-term load forecasting method using integrated CNN and LSTM network. – IEEE Access 9: 32436-32448.
[64] Rahman, A., Srikumar, V., Smith, A.D. (2018): Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks. – Applied Energy 212: 372-385.
[65] Rahman, M.M., Shakeri, M., Tiong, S.K., Khatun, F., Amin, N., Pasupuleti, J., Hasan, M.K. (2021): Prospective methodologies in hybrid renewable energy systems for energy prediction using artificial neural networks. – Sustainability 13(4): 28p.
[66] Rana, M., Koprinska, I. (2016): Forecasting electricity load with advanced wavelet neural networks. – Neurocomputing 182: 118-132.
[67] Rao, C., Zhang, Y., Wen, J., Xiao, X., Goh, M. (2023): Energy demand forecasting in China: A support vector regression-compositional data second exponential smoothing model. – Energy 263: 13p.
[68] Reddy, S., Akashdeep, S., Harshvardhan, R., Kamath, S. (2022): Stacking Deep learning and Machine learning models for short-term energy consumption forecasting. – Advanced Engineering Informatics 52: 10p.
[69] Regmi, N., Pandey, S.B. (2015): A regression analysis into Nepali ICT's energy consumption and its implications. – In 2015 9th International Conference on Software, Knowledge, Information Management and Applications (SKIMA), IEEE 8p.
[70] Román-Portabales, A., López-Nores, M., Pazos-Arias, J.J. (2021): Systematic review of electricity demand forecast using ANN-based machine learning algorithms. – Sensors 21(13): 23p.
[71] Saravanan, S., Khare, R., Umamaheswari, K., Khare, S., Gowda, B.K., Boopathi, S. (2024): AI and ML Adaptive Smart-Grid Energy Management Systems: Exploring Advanced Innovations. – In Principles and Applications in Speed Sensing and Energy Harvesting for Smart Roads, IGI Global 30p.
[72] Shamshirband, S., Rabczuk, T., Chau, K.W. (2019): A survey of deep learning techniques: application in wind and solar energy resources. – IEEE Access 7: 164650-164666.
[73] Shen, Y., Ma, Y., Deng, S., Huang, C.J., Kuo, P.H. (2021): An ensemble model based on deep learning and data preprocessing for short-term electrical load forecasting. – Sustainability 13(4): 21p.
[74] Somu, N., MR, G. R., Ramamritham, K. (2021): A deep learning framework for building energy consumption forecast. – Renewable and Sustainable Energy Reviews 137: 21p.
[75] Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., Liu, C. (2018): A survey on deep transfer learning. – In Artificial Neural Networks and Machine Learning-ICANN 2018: 27th International Conference on Artificial Neural Networks, Springer International Publishing 9p.
[76] Tawn, R., Browell, J. (2022): A review of very short-term wind and solar power forecasting. – Renewable and Sustainable Energy Reviews 153: 15p.
[77] Tien, P.W., Wei, S., Darkwa, J., Wood, C., Calautit, J.K. (2022): Machine learning and deep learning methods for enhancing building energy efficiency and indoor environmental quality-a review. – Energy and AI 10: 28p.
[78] Vu, D.H., Muttaqi, K.M., Agalgaonkar, A.P., Zahedmanesh, A., Bouzerdoum, A. (2021): Recurring multi-layer moving window approach to forecast day-ahead and week-ahead load demand considering weather conditions. – Journal of Modern Power Systems and Clean Energy 10(6): 1552-1562.
[79] Wang, F., Xuan, Z., Zhen, Z., Li, K., Wang, T., Shi, M. (2020): A day-ahead PV power forecasting method based on LSTM-RNN model and time correlation modification under partial daily pattern prediction framework. – Energy Conversion and Management 212: 14p.
[80] Wang, H., Lei, Z., Zhang, X., Zhou, B., Peng, J. (2019): A review of deep learning for renewable energy forecasting. – Energy Conversion and Management 198: 26p.
[81] Wei, Y., Zhang, X., Shi, Y., Xia, L., Pan, S., Wu, J., Han, M., Zhao, X. (2018): A review of data-driven approaches for prediction and classification of building energy consumption. – Renewable and Sustainable Energy Reviews 82: 1027-1047.
[82] Whang, S.E., Roh, Y., Song, H., Lee, J.G. (2023): Data collection and quality challenges in deep learning: A data-centric ai perspective. – The VLDB Journal 32(4): 791-813.
[83] Williams, L., Sovacool, B.K., Foxon, T.J. (2022): The energy use implications of 5G: Reviewing whole network operational energy, embodied energy, and indirect effects. – Renewable and Sustainable Energy Reviews 157: 18p.
[84] Xuan, W., Shouxiang, W., Qianyu, Z., Shaomin, W., Liwei, F. (2021): A multi-energy load prediction model based on deep multi-task learning and ensemble approach for regional integrated energy systems. – International Journal of Electrical Power & Energy Systems 126: 16p.
[85] Yagli, G.M., Yang, D., Srinivasan, D. (2019): Automatic hourly solar forecasting using machine learning models. – Renewable and Sustainable Energy Reviews 105: 487-498.
[86] Yang, A., Li, W., Yang, X. (2019): Short-term electricity load forecasting based on feature selection and Least Squares Support Vector Machines. – Knowledge-Based Systems 163: 159-173.
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