Smart City Gnosys

Smart city article details

Title	Multi-Term Electrical Load Forecasting Of Smart Cities Using A New Hybrid Highly Accurate Neural Network-Based Predictive Model
ID_Doc	38451
Authors	Safari A.; Kharrati H.; Rahimi A.
Year	2024
Published	Smart Grids and Sustainable Energy, 9, 1
DOI	http://dx.doi.org/10.1007/s40866-023-00188-9
Abstract	This paper presents FARHAN, a novel hybrid model designed to address the challenges of electrical load forecasting in smart grids. FARHAN combines descending neuron attention, long/short-term memory (LSTM), and Markov-simulated neural networks to optimize accuracy and analysis time for short-, mid-, and long-term smart grid planning decisions. FARHAN processes electricity load data efficiently by utilizing two LSTM blocks (LSTM.B1 & LSTM.B2) with attention layers, a 90% gain averager, and a Markov chain analyzer. The comparative analysis demonstrates FARHAN's superiority over traditional LSTM models and other methodologies, exhibiting remarkable Mean Absolute Percentage Errors (MAPEs) of 0.019162%, 0.0386%, and 0.039% for 14 years, annual, and monthly estimations, respectively. Root Mean Square Percentage Errors (RMSPEs) of 2.5%, 5.2%, and 1.2% and an overall R2 of 1 validate its exceptional accuracy. FARHAN's innovative approach establishes it as a robust and intelligent tool for enhancing electrical load forecasting in smart grids and energy systems, promising significant advancements in the field. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
Author Keywords	Artificial Neural Networks Smart Grid Load Forecasting; Intelligence Prediction-based Planning; Intelligent Analysis; Optimal Planning

Similar Articles

Id	Similarity	Authors	Title	Published
30937	0.907	Alsharekh M.F.; Habib S.; Dewi D.A.; Albattah W.; Islam M.; Albahli S.	Improving The Efficiency Of Multistep Short-Term Electricity Load Forecasting Via R-Cnn With Ml-Lstm	Sensors, 22, 18 (2022)
3739	0.903	Noaman S.A.; Ahmed A.M.S.; Salman A.D.	A Prediction Model Of Power Consumption In Smart City Using Hybrid Deep Learning Algorithm	International Journal on Informatics Visualization, 7, 4 (2023)
7417	0.896	Sunder R.; R S.; Paul V.; Punia S.K.; Konduri B.; Nabilal K.V.; Lilhore U.K.; Lohani T.K.; Ghith E.; Tlija M.	An Advanced Hybrid Deep Learning Model For Accurate Energy Load Prediction In Smart Building	Energy Exploration and Exploitation, 42, 6 (2024)
6990	0.894	Saeed A.; Asif R.M.; Rehman A.U.; Hassan S.R.; Bharany S.; Hamam H.	Ai-Based Energy Management And Prediction System For Smart Cities	Jordan Journal of Electrical Engineering, 11, 2 (2025)
38700	0.891	Amalou I.; Mouhni N.; Abdali A.	Multivariate Time Series Prediction By Rnn Architectures For Energy Consumption Forecasting	Energy Reports, 8 (2022)
12172	0.89	So D.; Oh J.; Jeon I.; Moon J.; Lee M.; Rho S.	Bigta-Net: A Hybrid Deep Learning-Based Electrical Energy Forecasting Model For Building Energy Management Systems	Systems, 11, 9 (2023)
8459	0.887	Alghamdi H.; Hafeez G.; Ali S.; Ullah S.; Khan M.I.; Murawwat S.; Hua L.-G.	An Integrated Model Of Deep Learning And Heuristic Algorithm For Load Forecasting In Smart Grid	Mathematics, 11, 21 (2023)
3113	0.884	Lu S.; Zeng Q.; Wu H.; Su J.; Liu X.; Tang H.	A New Power Load Forecasting Model (Sindrnn): Independently Recurrent Neural Network Based On Softmax Kernel Function	Proceedings - 21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019 (2019)
6009	0.883	Haring T.; Ahmadiahangar R.; Rosin A.; Korotko T.; Biechl H.	Accuracy Analysis Of Selected Time Series And Machine Learning Methods For Smart Cities Based On Estonian Electricity Consumption Forecast	Proceedings - 2020 IEEE 14th International Conference on Compatibility, Power Electronics and Power Engineering, CPE-POWERENG 2020 (2020)
28891	0.874	Akbarzadeh O.; Hamzehei S.; Attar H.; Amer A.; Fasihihour N.; Khosravi M.R.; Solyman A.A.	Heating-Cooling Monitoring And Power Consumption Forecasting Using Lstm For Energy-Efficient Smart Management Of Buildings: A Computational Intelligence Solution For Smart Homes	Tsinghua Science and Technology, 29, 1 (2024)