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Recent Patents on Engineering


ISSN (Print): 1872-2121
ISSN (Online): 2212-4047

Review Article

A Comprehensive Review of Modern Methods for Load Prediction in the Smart Grid

Author(s): Pushpa Attiwal* and Sanjeev Indora

Volume 18, Issue 4, 2024

Published on: 15 June, 2023

Article ID: e230423216155 Pages: 12

DOI: 10.2174/1872212118666230423143331

Price: $65


Load forecasting plays a crucial role in mitigating risks for utilities by predicting future usage of commodity markets transmission or supplied by the utility. To achieve this, various techniques such as price elastic demand, climate and consumer response, load analysis, and sustainable energy generation predictive modelling are used. As both supply and demand fluctuate, and weather and power prices can rise significantly during peak periods, accurate load forecasting becomes critical for utilities. By providing brief demand forecasts, load forecasting can assist in estimating load flows and making decisions that prevent overloading. Therefore, load forecasting is crucial in helping electric utilities make informed decisions related to power, load switching, voltage regulation, switching, and infrastructure development. Forecasting is a methodology used by electricity companies to forecast the amount of electricity or power production needed to maintain constant supply as well as load demand balance. It is required for the electrical industry to function properly. The smart grid is a new system that enables electricity providers and customers to communicate in real-time. The precise energy consumption sequence of the consumers is required to enhance the demand schedule. This is where predicting the future comes into play. Forecasting future power system load (electricity consumption) is a critical task in providing intelligence to the power grid. Accurate forecasting allows utility companies to allocate resources and assume system control in order to balance the same demand and availability for electricity. In this article, a study on load forecasting algorithms based on deep learning, machine learning, hybrid methods, bio-inspired techniques, and other techniques is carried out. Many other algorithms based on load forecasting are discussed in this study. Different methods of load forecasting were compared using three performance indices: RMSE (Root Mean Square Error), MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error), and Accuracy. Machine learning-based techniques showed a reduction of 9.17% in MAPE, 0.0429% in RMSE, and 5.23% in MSE, and achieved 90% accuracy. Deep learning-based techniques resulted in a 9.61% decrease in MAPE and achieved 91% accuracy. Bioinspired techniques provided a reduction of 9.66% in MAPE, 0.026% in RMSE, and 5.24% in MSE, and achieved 95% accuracy. These findings concluded that optimization techniques are more encouraging in predicting load demand and, as a result, can represent a reliable decision-making tool.

Keywords: Smart grid, load forecasting, energy management, machine learning, network, smart home, electricity load forecasting, smart metering.

Graphical Abstract
X. Fang, S. Misra, G. Xue, and D. Yang, "Smart grid - The new and improved power grid: A survey", IEEE Commun. Surv. Tutor., vol. 14, no. 4, pp. 944-980, 2012.
R. Bayindir, I. Colak, G. Fulli, and K. Demirtas, "Smart grid technologies and applications", Renew. Sustain. Energy Rev., vol. 66, pp. 499-516, 2016.
M. Ali, M. Adnan, M. Tariq, H.V. Poor, H.V. Poor, and L. Fellow, "Load forecasting through estimated parametrized based fuzzy inference system in smart grids", IEEE Trans. Fuzzy Syst., vol. 29, no. 1, pp. 156-165, 2021.
H. Daneshi, M. Shahidehpour, and A.L. Choobbari, "Long-term load forecasting in electricity market", In 2008 IEEE International Conference on Electro/Information Technology, Ames, IA, USA, 2008, pp. 395-400
T. Anwar, B. Sharma, K. Chakraborty, and H. Sirohia, "Introduction to load forecasting", Int. J. Pure Appl. Math., vol. 119, no. 15, pp. 1527-1538, 2018.
A. Gopstein, C. Nguyen, C. O’Fallon, N. Hastings, and D. Wollman, NIST Framework and Roadmap for Smart Grid Interoperability Standards, Release 4.0, Special Publication (NIST SP)., Gaithersburg, MD: National Institute of Standards and Technology, 2021.
P.P. Parikh, M.G. Kanabar, and T.S. Sidhu, "Opportunities and challenges of wireless communication technologies for smart grid applications", In: IEEE PES General Meeting, Minneapolis, USA, 2010, p. 1-7.
A.B.M.S. Ali, Smart Grids: Opportunities, Developments, and Trends., Springer: London, 2013.
S. Hosein, and P. Hosein, "Load forecasting using deep neural networks", 2017 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington, D.C, USA, vol. 2017, 2017, p, p. 1-5.
S. Fathi, R. Srinivasan, A. Fenner, and S. Fathi, "Machine learning applications in urban building energy performance forecasting: A systematic review", Renew. Sustain. Energy Rev., vol. 133, p. 110287, 2020.
S. Xia, X. Luo, and K.W. Chan, "A framework for self-healing smart grid with incorporation of multi-agents", Energy Procedia, vol. 61, pp. 2123-2126, 2014.
O. Majeed Butt, M. Zulqarnain, and T. Majeed Butt, "Recent advancement in smart grid technology: Future prospects in the electrical power network", Ain Shams Eng. J., vol. 12, no. 1, pp. 687-695, 2021.
A. Almalaq, and G. Edwards, "A review of deep learning methods applied on load forecasting", 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), Cancun, Mexico, 2017, pp. 511-516.
F. Abdullah Asuhaimi, S. Bu, P. Valente Klaine, and M.A. Imran, "Channel access and power control for energy-efficient delay-aware heterogeneous cellular networks for smart grid communications using deep reinforcement learning", IEEE Access, vol. 7, pp. 133474-133484, 2019.
H. Mortaji, S.H. Ow, M. Moghavvemi, and H.A.F. Almurib, "Load shedding and smart-direct load control using internet of things in smart grid demand response management", IEEE Trans. Ind. Appl., vol. 53, no. 6, pp. 5155-5163, 2017.
C.P. Vineetha, and C.A. Babu, "Smart grid challenges, issues and solutions", In 2014 International Conference on Intelligent Green Building and Smart Grid (IGBSG), Taipei, Taiwan, 2014, pp. 1-4
T.S. Bomfim, "Evolution of machine learning in smart grids", In 2020 IEEE 8th International Conference on Smart Energy Grid Engineering (SEGE), Oshawa, ON, Canada, 2020, p. 82-87
H.T. Zhang, F.Y. Xu, and L. Zhou, "Artificial neural network for load forecasting in smart grid", 2010 International Conference on Machine Learning and Cybernetics, Qingdao, China, 2010, pp. 3200-3205.
X. Pan, and B. Lee, "A comparison of support vector machines and artificial neural networks for mid-term load forecasting", In 2012 IEEE International Conference on Industrial Technology, Athens, Greece, 2012, pp. 95-101
M.Q. Raza, and A. Khosravi, "A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings", Renew. Sustain. Energy Rev., vol. 50, pp. 1352-1372, 2015.
A. Mukherjee, P. Mukherjee, N. Dey, D. De, and B.K. Panigrahi, "Lightweight sustainable intelligent load forecasting platform for smart grid applications", Sustainable Computing: Informatics and Systems, vol. 25, pp. 100356-100356, 2020.
A.K. Bashir, S. Khan, and B. Prabadevi, "Comparative analysis of machine learning algorithms for prediction of smart grid stability", Int. Trans. Electr. Energy Syst., vol. 31, no. 9, p. e12706, 2021.
S. Rai, and M. De, "Analysis of classical and machine learning based short-term and mid-term load forecasting for smart grid", Int. J. Sustain. Energy, vol. 40, no. 9, pp. 821-839, 2021.
A. Haque, and S. Rahman, "Short-term electrical load forecasting through heuristic configuration of regularized deep neural network", Appl. Soft Comput., vol. 122, p. 108877, 2022.
H.H.H. Aly, "A proposed intelligent short-term load forecasting hybrid models of ANN, WNN and KF based on clustering techniques for smart grid", Electr. Power Syst. Res., vol. 182, no. February, pp. 106191-106191, 2020.
L. Wang, S. Mao, B.M. Wilamowski, and R.M. Nelms, "Ensemble learning for load forecasting", IEEE Trans. Green Commun. Netw., vol. 4, no. 2, pp. 616-628, 2020.
A.A. Khan, M. Alamaniotis, V. Prakash, and H. Fontenot, "Ensemble method for short-term load forecasting using Lstm, Svr, and Fnn and taking into account seasonal dependency (OR-20-C052)", In: ASHRAE Transactions, Orlando, FL, USA, 2020.
R. Jiao, S. Wang, T. Zhang, H. Lu, H. He, and B.B. Gupta, "Adaptive feature selection and construction for day-ahead load forecasting use deep learning method", IEEE Trans. Netw. Serv. Manag., vol. 18, no. 4, pp. 4019-4029, 2021.
W. Kong, Z.Y. Dong, Y. Jia, D.J. Hill, Y. Xu, and Y. Zhang, "Short-term residential load forecasting based on LSTM recurrent neural network", IEEE Trans. Smart Grid, vol. 10, no. 1, pp. 841-851, 2019.
K. Yan, W. Li, Z. Ji, M. Qi, and Y. Du, "A Hybrid LSTM neural network for energy consumption forecasting of individual households", IEEE Access, vol. 7, pp. 157633-157642, 2019.
A.S. Khwaja, A. Anpalagan, M. Naeem, and B. Venkatesh, "Joint bagged-boosted artificial neural networks: Using ensemble machine learning to improve short-term electricity load forecasting", Electr. Power Syst. Res., vol. 179, p. 106080, 2020.
L. Jiang, X. Wang, W. Li, L. Wang, X. Yin, and L. Jia, "Hybrid multitask multi-information fusion deep learning for household short-term load forecasting", IEEE Transactions on. Smart Grid, vol. 12, no. 6, 2021.
J. Byun, I. Hong, B. Kang, and S. Park, "A smart energy distribution and management system for renewable energy distribution and context-aware services based on user patterns and load forecasting", IEEE Trans. Consum. Electron., vol. 57, no. 2, pp. 436-444, 2011.
A. Ahmad, N. Javaid, M. Guizani, N. Alrajeh, and Z.A. Khan, "An accurate and fast converging short-term load forecasting model for industrial applications in a smart grid", IEEE Trans. Industr. Inform., vol. 13, no. 5, pp. 2587-2596, 2017.
L.J. Dong, Z. Tang, and Xb. Li, "Discrimination of mining microseismic events and blasts using convolutional neural networks and original waveform", J. Cent. South Univ., vol. 27, pp. 3078-3089, 2020.
S. Ghosh, and D. Chatterjee, "Artificial bee colony optimization based non-intrusive appliances load monitoring technique in a smart home", IEEE Trans. Consum. Electron., vol. 67, no. 1, pp. 77-86, 2021.
S.M.J. Jalali, S. Ahmadian, A. Khosravi, M. Shafie-khah, S. Nahavandi, and J.P.S. Catalão, "A novel evolutionary-based deep convolutional neural network model for intelligent load forecasting", IEEE Trans. Industr. Inform., vol. 17, no. 12, pp. 8243-8253, 2021.
L. Haghnegahdar, and Y. Wang, "A whale optimization algorithm-trained artificial neural network for smart grid cyber intrusion detection", Neural Comput. Appl., vol. 32, no. 13, pp. 9427-9441, 2020.
S.I. Abba, B.G. Najashi, A. Rotimi, B. Musa, N. Yimen, S.J. Kawu, S.M. Lawan, and M. Dagbasi, "Emerging Harris Hawks Optimization based load demand forecasting and optimal sizing of stand-alone hybrid renewable energy systems– A case study of Kano and Abuja, Nigeria", Res. Eng., vol. 12, p. 100260, 2021.
H. Hu, X. Xia, Y. Luo, C. Zhang, M.S. Nazir, and T. Peng, "Development and application of an evolutionary deep learning framework of LSTM based on improved grasshopper optimization algorithm for short-term load forecasting", J. Build. Eng., vol. 57, p. 104975, 2022.
M. Talaat, M.A. Farahat, N. Mansour, and A.Y. Hatata, "Load forecasting based on grasshopper optimization and a multilayer feed-forward neural network using regressive approach", Energy, vol. 196, p. 117087, 2020.
Z. Liu, P. Jiang, J. Wang, and L. Zhang, "Ensemble forecasting system for short-term wind speed forecasting based on optimal sub-model selection and multi-objective version of mayfly optimization algorithm", Expert Syst. Appl., vol. 177, p. 114974, 2021.
P. Vasanthkumar, N. Senthilkumar, K.S. Rao, A.S.M. Metwally, I.M.R. Fattah, T. Shaafi, and V.S. Murugan, "Improving energy consumption prediction for residential buildings using Modified Wild Horse Optimization with Deep Learning model", Chemosphere, vol. 308, no. 1, p. 136277, 2022.

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