2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 14 , Issue 1 , PP: 90-101, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
E. T. Sivadasan 1 * , N. Mohana Sundaram 2 , R. Santhosh 3
Doi: https://doi.org/10.54216/JISIoT.140107
Forecasting energy demand is essential for efficient grid management as it promotes steady operations, efficient markets, and sustainable energy practices. In this study, previously observed, evenly spaced energy consumption data are analysed using recurrent neural networks based on Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) architectures to extract important insights, features, and remarkable patterns. First, the study examines the influence of meteorological features on energy consumption. The most significant meteorological features are determined by computing the MIC and Pearson's correlation coefficient. The selected features are then combined with historical energy consumption data to feed the neural network. Second, to improve and optimise the performance of the proposed models, two technical indicators - the daily energy usage average and the simple moving average - are considered. The following are some instances of comparisons in terms of prediction accuracy: (1) The MAPE of the proposed model is 2.47, whereas that of the current model is 4.03. (2) The MAPE of the existing model is 25.83, whereas the proposed solution is 18.68. (3) The MAPE of the suggested model is 24.8, while the MAPE of the current model is 26.6. (4) The MAPE of the present model is 4.77, whereas the suggested approach's is 4.42.
Time Series , Energy Forecasting , MIC , GRU , LSTM
[1] Ahmad O. Aseeri, ”Effective RNN-Based Forecasting Methodology Design for Improving Short-Term Power Load Forecasts: Application to Large-Scale Power-Grid Time Series”, Journal of Computational Science Volume 68, April 2023, 101984.
[2] Xianlun Tang , Hongxu Chen, Wenhao Xiang , Jingming Yang, Mi Zou, ”Short-Term Load Forecasting Using Channel and Temporal Attention Based Temporal Convolutional Network”, Electric Power Systems Research 205 (2022) 107761.
[3] Seyed Mohammad Jafar Jalali, Student Member, IEEE, Sajad Ahmadian, Abbas Khosravi, Senior Member, IEEE, Miadreza Shafie-khah, Senior Member, IEEE, Saeid Nahavandi, Fellow, IEEE, and Joao P.S. Catalao, Senior Member, IEEE, ”A Novel Evolutionary-based Deep Convolutional Neural Network Model for Intelligent Load Forecasting”, 12 March 2021, IEEE Transactions on Industrial Informatics (Volume: 17, Issue: 12, December 2021).
[4] Muhammad Qamar Raza, Abbas Khosravi, ”A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings”, Renewable and Sustainable Energy Reviews, Volume 50, October 2015, Pages 1352-1372.
[5] E. T. Sivadasan, N. Mohana Sundaram, R.Santhosh. "Stock market forecasting using deep learning with long short-term memory and gated recurrent unit", Soft Computing, 2024
[6] Hao, Jin, “Input selection using mutual information - applications to time series prediction”, 2004 - 2005, Master’s thesis, Helsinki University of tehnology, Department of Computer and Information Science.
[7] Milos B. Stojanovic, Milos M. Bozic, and Milena M. Stankovic, ”MidTerm Load Forecasting Using Recursive Time Series Prediction Strategy With Support Vector Machines”, FACTA UNIVERSITATIS (NIS), SER.: ELEC. ENERG. vol. 23, no. 3, Decemer 2010, 287-298.
[8] J.P.S. Catalao, S.J.P.S. Mariano, V.M.F. Mendes, L.A.F.M. Ferreira, ”Short-term electricity prices forecasting in a competitive market: A neural network approach”, Electric Power Systems Research 77 (2007) 1297–1304 , ELSEVIER.
[9] Henrique Steinherz Hippert, Carlos Eduardo Pedreira, and Reinaldo Castro Souza, ”Neural Networks for Short-Term Load Forecasting: A Review and Evaluation”, IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 16, NO. 1, FEBRUARY 2001.
[10] C. S. Manigandaa,V. D. Ambeth Kumar,G. Ragunath,R. Venkatesan,N. Senthil Kumar. (2023). De-Noising and Segmentation of Medical Images using Neutrophilic Sets. Journal of Fusion: Practice and Applications, 11 ( 2 ), 111-123.
[11] Shu Fan, Senior Member, IEEE, and Rob J Hyndman, ”Short-term load forecasting based on a semi-parametric additive model”, 18 August 2011 , IEEE Transactions on Power Systems ( Volume: 27, Issue: 1, February 2012).
[12] Nima Amjady, Senior Member, IEEE, and Ali Daraeepour, Member, IEEE, ”Midterm Demand Prediction of Electrical Power Systems Using a New Hybrid Forecast Technique”, IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 26, NO. 2, MAY 2011.
[13] S. Hemamalini ,V. D. Ambeth Kumar ,R. Venkatesan,S. Malathi. (2023). Relevance Mapping based CNN model with OSR-FCA Technique for Multi-label DR Classification. Journal of Fusion: Practice and Applications, 11 ( 2 ), 90-110.
[14] Wei-ting XU, Yun-ling Wang, Ting Li, Quan Tang, Jin-fang Zhang, Li Shen, Mi Zhu, ”Long Term Intelligent Load Forecasting Method Considering the Expectation of Power Market Transaction”, 2017 29th Chinese Control And Decision Conference (CCDC), Date Added to IEEE Xplore: 17 July 2017, ISBN:978-1-5090-4658-4.
[15] S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation, vol. 9, no. 8, pp. 1735–1780, 1997
[16] Sherubha, “Graph Based Event Measurement for Analyzing Distributed Anomalies in Sensor Networks”, Sådhanå(Springer), 45:212, https://doi.org/10.1007/s12046-020-01451-w
[17] Piyush K. Pareek, Pixel Level Image Fusion in Moving objection Detection and Tracking with Machine Learning “,Fusion: Practice and Applications, Volume 2 , Issue 1 , PP: 42-60, 2020
[18] Shivam Grover, Kshitij Sidana, Vanita Jain, “Egocentric Performance Capture: A Review”, Fusion: Practice and Applications, Volume 2, Issue 2 , PP: 64-73, 2020.
[19] Abdel Nasser H. Zaied, Mahmoud Ismail and Salwa El- Sayed, A Survey on Meta-heuristic Algorithms for Global Optimization Problems, Journal of Intelligent Systems and Internet of Things,Volume 1 , Issue 1 , PP: 48-60, 2020
[20] Mahmoud H.Alnamoly, Ahmed M. Alzohairy, Ibrahim M. El-Henawy, “A survey on gel images analysis software tools, Journal of Intelligent Systems and Internet of Things,Volume 1 , Issue 1 , PP: 40-47, 2021.
[21] Kumar, V.D.A., Sharmila, S., Kumar, A. et al. (2023). A novel solution for finding postpartum haemorrhage using fuzzy neural techniques. Neural Comput & Applic. 35(33), 23683–23696
[22] Guanzhi Li, Aining Zhang, Qizhi Zhang, Di Wu, and Choujun Zhan, Member, IEEE. ”Pearson Correlation Coefficient-Based Performance Enhancement of Broad Learning System for Stock Price Prediction”, IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS—II: EXPRESS BRIEFS, VOL. 69, NO. 5, MAY 2022.
[23] Sathya Preiya, V., and V. D. Ambeth Kumar. (2023). Deep Learning-Based Classification and Feature Extraction for Predicting Pathogenesis of Foot Ulcers in Patients with Diabetes. Diagnostics 13(12), 1983.
[24] Balakrishnan, Chitra, and V. D. Ambeth Kumar. (2023). IoT-Enabled Classification of Echocardiogram Images for Cardiovascular Disease Risk Prediction with Pre-Trained Recurrent Convolutional Neural Networks. Diagnostics 13(4), 775.
[25] Hemamalini, Selvamani, and Visvam Devadoss Ambeth Kumar. (2022). Outlier Based Skimpy Regularization Fuzzy Clustering Algorithm for Diabetic Retinopathy Image Segmentation. Symmetry, 14(12), 2512.
[26] R. Dey, F. M. Salem, ”Gate-Variants of Gated Recurrent Unit ( GRU ) Neural Networks”, Neural and Evolutionary Computing, arXiv:1701.05923.
[27] M. Hiransha, E.A. Gopalakrishnan, V.K. Menon, K.P. Soman, ”NSE Stock Market Prediction using Deep-Learning Models”, Procedia computer science, vol. 132, pp. 1351–1362, 2018.
[28] Yi Qiao; Kaiwen Xu; Kai Zhou, ”Research on time series based on improved LSTM”, 2023 IEEE 3rd International Conference on Power, Electronics and Computer Applications (ICPECA), 29 March 2023.
