Short-Term Electrical Load Forecasting in Power Systems Using Deep Learning Techniques

Yıl 2023, Cilt: 27 Sayı: 5, 1111 - 1121, 18.10.2023

Nihat Pamuk

https://doi.org/10.16984/saufenbilder.1256743

Öz

The use of big data in deep neural networks has recently surpassed traditional machine learning techniques in many application areas. The main reasons for the use of deep neural networks are the increase in computational power made possible by graphics processing units and tensor processing units, and the new algorithms created by recurrent neural networks and CNNs. In addition to traditional machine learning methods, deep neural networks have applications in anticipating electricity load. Using a real dataset for one-step forecasting, this article compares three deep learning algorithms for short-term power load forecasting: LSTM, GRUs, and CNN. The statistics come from the Turkish city of Zonguldak and include hourly electricity usage loads and temperatures over a period of three years, commencing in 2019 and ending in 2021. The mean absolute percentage error is used to compare the performances of the techniques. Forecasts are made for twelve representative months from each season. The main reason for the significant deviations in the forecasts for January, May, September, and December is the presence of religious and national holidays in these months. This was solved by adding the information obtained from religious and national holidays to the modeling. This is not to say that CNNs are not good at capturing long-term dependencies and modeling sequential data. In all experiments, LSTM, GRUs and encoder-decoder LSTM outperformed simple CNN designs. In the future, these new architectural methods can be applied to long- or short-term electric charge predictions and their results can be compared to LSTM, GRUs and their variations.

Anahtar Kelimeler

Short-term forecasting, electricity load, graphical process units, tensor process units

Kaynakça

• S. Fan, R. J. Hyndman, “Short-Term Load Forecasting based on a Semi-Parametric Additive Model,” IEEE Transactions on Power Systems, vol. 27, no. 1, pp. 134-141, 2011.
• N. Pamuk, “Empirical Analysis of Causal Relationship between Electricity Production and Consumption Demand in Turkey Using Cobb-Douglas Model,” Journal of Polytechnic, vol. 19, no. 4, pp. 415-420, 2016.
• K. Kaysal, E. Akarslan, F. O. Hocaoglu, “Comparison of Machine Learning Methods in Turkey’s Short-Term Electricity Load Demand Estimation,” Bilecik Seyh Edebali University Journal of Science, vol. 9, no. 2, pp. 693-702, 2022.
• A. Allee, N. J. Williams, A. Davis, P. Jaramillo, “Predicting Initial Electricity Demand in off-grid Tanzanian Communities Using Customer Survey Data and Machine Learning Models,” Energy for Sustainable Development, vol. 62, pp. 56-66, 2021.
• J. Huo, T. Shi, J. Chang, “Comparison of Random Forest and SVM for Electrical Short-Term Load Forecast with Different Data Sources,” 7th IEEE International Conference on Software Engineering and Service Science (ICSESS), 26-28 August, Beijing, China, 2016, pp. 1077-1080.
• G. Mitchell, S. Bahadoorsingh, N. Ramsamooj, C. Sharma, “A Comparison of Artificial Neural Networks and Support Vector Machines for Short-Term Load Forecasting Using Various Load Types,” IEEE Manchester PowerTech, 18-22 June, Manchester, UK, 2017, 17044934.
• S. H. Rafi, S. R. Deeba, E. Hossain, “A Short-Term Load Forecasting Method Using Integrated CNN and LSTM Network,” IEEE Access, vol. 9, pp. 32436-32448, 2021.
• P. Singh, P. Dwivedi, V. Kant, “A Hybrid Method Based on Neural Network and Improved Environmental Adaptation Method Using Controlled Gaussian Mutation with Real Parameter for Short-Term Load Forecasting,” Energy, vol. 174, pp. 460-477, 2019.
• J. Lin, J. Ma, J. Zhu, Y. Cui, “Short-Term Load Forecasting Based on LSTM Networks Considering Attention Mechanism,” International Journal of Electrical Power & Energy Systems, vol. 137, 107818, 2022.
• C. Yang, Z. An, H. Zhu, X. Hu, K. Zhang, K. Xu, Y. Xu, “Gated Convolutional Networks with Hybrid Connectivity for Image Classification,” In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 07, pp. 12581-12588, 2020.
• Y. Mo, Q. Wu, X. Li, B. Huang, “Remaining Useful Life Estimation via Transformer Encoder Enhanced by A Gated Convolutional Unit,” Journal of Intelligent Manufacturing, vol. 32, pp. 1997-2006, 2021.
• Y. Wang, M. Liu, Z. Bao, S. Zhang, “Short-Term Load Forecasting with Multi-Source Data Using Gated Recurrent Unit Neural Networks,” Energies, vol. 11, no. 5, 1138, 2018.
• Z. Ferdoush, B. N. Mahmud, A. Chakrabarty, J. Uddin, “A Short-Term Hybrid Forecasting Model for Time Series Electrical-Load Data Using Random Forest and Bidirectional Long Short-Term Memory,” International Journal of Electrical and Computer Engineering, vol. 11, no. 1, pp. 763-771, 2021.
• R. Wan, S. Mei, J. Wang, M. Liu, F. Yang, “Multivariate Temporal Convolutional Network: A Deep Neural Networks Approach for Multivariate Time Series Forecasting,” Electronics, vol. 8, no. 8, 876, 2019.
• J. C. Nunez, R. Cabido, J. J. Pantrigo, A. S. Montemayor, J. F. Velez, “Convolutional Neural Networks and Long Short-Term Memory for Skeleton-Based Human Activity and Hand Gesture Recognition,” Pattern Recognition, vol. 76, pp. 80-94, 2018.
• C. Tian, J. Ma, C. Zhang, P. Zhan, “A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network,” Energies, vol. 11, no. 12, 3493, 2018.
• P. W. Khan, Y. C. Byun, A. J. Lee, D. H. Kang, J. Y. Kang, H. S. Park, “Machine Learning-Based Approach to Predict Energy Consumption of Renewable and Nonrenewable Power Sources,” Energies, vol. 13, no. 18, 4870, 2020.
• H. M. Al-Hamadi, S. A. Soliman, “Long-Term/Mid-Term Electric Load Forecasting Based on Short-Term Correlation and Annual Growth,” Electric Power Systems Research, vol. 74, no. 3, pp. 353-361, 2005.
• N. Edison, A. C. Aranha, J. Coelho, “Probabilistic Methodology for Technical and Non-Technical Losses Estimation in Distribution System,” Electric Power Systems Research, vol. 97, no. 11, pp. 93-99, 2013.
• N. Pamuk, “Determination of Chaotic Time Series in Dynamic Systems,” Journal of Balıkesir University Institute of Science and Technology, vol. 15, no. 1, pp. 78-92, 2013.
• A. Singh, G. C. Mishra, “Application of Box-Jenkins Method and Artificial Neural Network Procedure for Time Series Forecasting of Prices,” Statistics in Transition new series, vol. 16, no. 1, 83-96, 2015.
• N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, R. Salakhutdinov, “Dropout: A Simply Way to Prevent Neural Networks From Overfitting,” The Journal of Machine Learning Research, vol. 15, no. 1, pp. 1929-1958, 2014.
• N. Yampikulsakul, E. Byon, S. Huang, S. Sheng, M. You, “Condition Monitoring of Wind Power System with Nonparametric Regression Analysis,” IEEE Transactions on Energy Conversion, vol. 29, no. 2, pp. 288-299, 2014.
• S. Li, Y. Han, X. Yao, S. Yingchen, J. Wang, Q. Zhao, “Electricity Theft Detection in Power Grids with Deep Learning and Random Forests,” Journal of Electrical and Computer Engineering, vol. 2019, 4136874, pp. 1-12, 2019.
• Z. Zheng, Y. Yang, X. Niu, H. N. Dai, Y. Zhou, “Wide and Deep Convolutional Neural Networks for Electricity-Theft Detection to Secure Smart Grids,” IEEE Transactions on Industrial Informatics, vol. 14, no. 4, pp. 1606-1615, 2018.
• A. H. Nizar, Z. Y. Dong, Y. Wang, A. N. Souza, “Power Utility Nontechnical Loss Analysis with Extreme Learning Machine Method,” IEEE Transactions on Power Systems, vol. 23, no. 3, pp. 946-955, 2008.