References
- Abedinia, O., M. Zareinejad, M. H. Doranehgard, G. Fathi, and N. Ghadimi. 2019. “Optimal Offering and Bidding Strategies of Renewable Energy Based Large Consumer Using a Novel Hybrid Robust- Stochastic Approach.” Journal of Cleaner Production 215: 878–889. doi:https://doi.org/10.1016/J.JCLEPRO.2019.01.085.
- Adhikari, R., and R. K. Agrawal. 2013. “An Introductory Study on Time Series Modeling and Forecasting.” arXiv Preprint arXiv 1302: 6613.
- AEMO report. 2019. Australian Energy Market Operator (AEMO) 2019. Viewed Jan. 2020, https://www.aemo.com.au
- AER Report. 2018. Wholesale Electricity Market Performance Report December 2018. (Australian Energy Regulator). viewed Dec. 2019. https://www.aer.gov.au/wholesale-markets/market-performance/aer-wholesale-electricity-market-performance-report-2018
- Alam, S., and M. Ali. 2020. “Equation Based New Methods for Residential Load Forecasting.” Energies 13 (23): 6378. doi:https://doi.org/10.3390/en13236378.
- Andalib, A., and F. Atry. 2009. “Multi-step Ahead Forecasts for Electricity Prices Using NARX: A New Approach, a Critical Analysis of One-step Ahead Forecasts.” Energy Conversion and Management 50 (3): 739–747. doi:https://doi.org/10.1016/j.enconman.2008.09.040.
- Bouktif, S., A. Fiaz, A. Ouni, and M. A. Serhani. 2018. “Optimal Deep Learning Lstm Model for Electric Load Forecasting Using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches.” Energies 11 (7): 1636. doi:https://doi.org/10.3390/en11071636.
- Brownlee, J. 2018. Deep learning for time series forecasting: predict the future with MLPs, CNNs and LSTMs in Python, Machine Learning Mastery.
- Chen, B.-J., M.-W. Chang, and C.-J. Lin. 2004. “Load Forecasting Using Support Vector Machines: A Study on EUNITE Competition 2001.” IEEE Transactions on Power Systems 19 (4): 1821–1830. doi:https://doi.org/10.1109/TPWRS.2004.835679.
- Dehalwar, V., A. Kalam, M. L. Kolhe, and A. Zayegh. 2016. “Electricity Load Forecasting for Urban Area Using Weather Forecast Information.” In 2016 IEEE International Conference on Power and Renewable Energy (ICPRE), Shanghai, China, 355–359. IEEE. doi:https://doi.org/10.1109/ICPRE.2016.7871231.
- Ertugrul, Ö. F. 2016. “Forecasting Electricity Load by a Novel Recurrent Extreme Learning Machines Approach.” International Journal of Electrical Power & Energy Systems 78: 429–435. doi:https://doi.org/10.1016/j.ijepes.2015.12.006.
- Gao, W., A. Darvishan, M. Toghani, M. Mohammadi, O. Abedinia, and N. Ghadimi. 2019. “Different States of Multi-block Based Forecast Engine for Price and Load Prediction.” International Journal of Electrical Power & Energy Systems 104: 423–435. doi:https://doi.org/10.1016/j.ijepes.2018.07.014.
- Garreta, R., and G. Moncecchi. 2013. Learning Scikit-learn: Machine Learning in Python. Birmingham: Packt Publishing .
- Ghadimi, N., A. Akbarimajd, H. Shayeghi, and O. Abedinia. 2018. “Two Stage Forecast Engine with Feature Selection Technique and Improved Meta-heuristic Algorithm for Electricity Load Forecasting.” Energy 161: 130–142. doi:https://doi.org/10.1016/j.energy.2018.07.088.
- Ghasemi, A., H. Shayeghi, M. Moradzadeh, and M. Nooshyar. 2016. “A Novel Hybrid Algorithm for Electricity Price and Load Forecasting in Smart Grids with Demand-side Management.” Applied Energy 177: 40–59. doi:https://doi.org/10.1016/j.apenergy.2016.05.083.
- Gong, G., X. An, N. K. Mahato, S. Sun, S. Chen, and Y. Wen. 2019. “Research on Short-term Load Prediction Based on Seq2seq Model.” Energies 12 (16): 3199. doi:https://doi.org/10.3390/en12163199.
- Graves, A., A.-R. Mohamed, and G. Hinton. 2013. “Speech Recognition with Deep Recurrent Neural Networks.” In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada, 6645–6649. IEEE. doi:https://doi.org/10.1109/ICASSP.2013.6638947.
- Hochreiter, S., and J. Schmidhuber. 1997a. “Long Short-term Memory.” Neural Computation 9 (8): 1735–1780. doi:https://doi.org/10.1162/neco.1997.9.8.1735.
- Hochreiter, S., and J. Schmidhuber. 1997b. “LSTM Can Solve Hard Long Time Lag Problems.” Advances in Neural Information Processing Systems, Denver, CO, USA, Denver, CO, USA, 2–5 December 2–5, 1996, 473–479.
- Hong, T., M. Gui, M. E. Baran, and H. L. Willis. 2010. “Modeling and Forecasting Hourly Electric Load by Multiple Linear Regression with Interactions.” IEEE PES General Meeting 1–8. IEEE. doi:https://doi.org/10.1109/PES.2010.5589959.
- Hu, M. Y., G. Zhang, C. X. Jiang, and B. E. Patuwo. 1999. “A Cross-validation Analysis of Neural Network Out-of-sample Performance in Exchange Rate Forecasting.” Decision Sciences 30 (1): 197–216. doi:https://doi.org/10.1111/j.1540-5915.1999.tb01606.x.
- Jabir, H. J., J. Teh, D. Ishak, and H. Abunima. 2018. “Impacts of Demand-side Management on Electrical Power Systems: A Review.” Energies 11 (5): 1050. doi:https://doi.org/10.3390/en11051050.
- Jiang, P., F. Liu, and Y. Song. 2017. “A Hybrid Forecasting Model Based on Date-framework Strategy and Improved Feature Selection Technology for Short-term Load Forecasting.” Energy 119: 694–709. doi:https://doi.org/10.1016/j.energy.2016.11.034.
- Khodaei, H., M. Hajiali, A. Darvishan, M. Sepehr, and N. Ghadimi. 2018. “Fuzzy-based Heat and Power Hub Models for Cost-emission Operation of an Industrial Consumer Using Compromise Programming.” Applied Thermal Engineering 137: 395–405. doi:https://doi.org/10.1016/j.applthermaleng.2018.04.008.
- Khuntia, S. R., J. L. Rueda, and M. A. Van der Meijden. 2018. “Long-term Electricity Load Forecasting considering Volatility Using Multiplicative Error Model.” Energies 11 (12): 3308. doi:https://doi.org/10.3390/en11123308.
- Kourtis, G., I. Hadjipaschalis, and A. Poullikkas. 2011. “An Overview of Load Demand and Price Forecasting Methodologies.” International Journal of Energy & Environment 2 (1): 123-150.
- Kuo, P.-H., and C.-J. Huang. 2018. “An Electricity Price Forecasting Model by Hybrid Structured Deep Neural Networks.” Sustainability 10 (4): 1280. doi:https://doi.org/10.3390/su10041280.
- Lau, E., L. Sun, and Q. Yang. 2019. “Modelling, Prediction and Classification of Student Academic Performance Using Artificial Neural Networks.” SN Applied Sciences 1 (9): 1–10. doi:https://doi.org/10.1007/s42452-019-0884-7.
- Le, X.-H., H. V. Ho, G. Lee, and S. Jung. 2019. “Application of Long Short-term Memory (LSTM) Neural Network for Flood Forecasting.” Water 11 (7): 1387. doi:https://doi.org/10.3390/w11071387.
- LeCun, Y., Y. Bengio, and G. Hinton. 2015. “Deep Learning.” nature 521 (7553): 436–444. doi:https://doi.org/10.1038/nature14539.
- Li, C., Z. Ding, D. Zhao, J. Yi, and G. Zhang. 2017. “Building Energy Consumption Prediction: An Extreme Deep Learning Approach.” Energies 10 (10): 1525. doi:https://doi.org/10.3390/en10101525.
- Li, K., and T. Zhang. 2018. “Forecasting Electricity Consumption Using an Improved Grey Prediction Model.” Information 9 (8): 204. doi:https://doi.org/10.3390/info9080204.
- Lipton, Z. C., J. Berkowitz, and C. Elkan. 2015. “A Critical Review of Recurrent Neural Networks for Sequence Learning.” arXiv Preprint arXiv:1506.00019.
- Marino, D. L., K. Amarasinghe, and M. Manic 2016. “Building Energy Load Forecasting Using Deep Neural Networks.” IECON 2016–42nd Annual Conference of the IEEE Industrial Electronics Society, IEEE, Florence, Italy, October 23–26, 2016, pp. 7046–7051. doi: https://doi.org/10.1109/IECON.2016.7793413.
- Medennikov, I., and A. Bulusheva 2016. “LSTM-based Language Models for Spontaneous Speech Recognition.” International Conference on Speech and Computer, Budapest, Hungary, August 23-27, 2016, Springer, pp. 469–475. doi: https://doi.org/10.1007/978-3-319-43958-7_56.
- Mikolov, T., A. Joulin, S. Chopra, M. Mathieu, and M. A. Ranzato. 2014. “Learning Longer Memory in Recurrent Neural Networks.” arXiv Preprint arXiv:1412.7753.
- Mohandes, M. 2002. “Support Vector Machines for Short-term Electrical Load Forecasting.” International Journal of Energy Research 26 (4): 335–345. doi:https://doi.org/10.1002/er.787.
- Mujeeb, S., N. Javaid, M. Akbar, R. Khalid, O. Nazeer, and M. Khan 2018. “Big Data Analytics for Price and Load Forecasting in Smart Grids.” International Conference on Broadband and Wireless Computing, Communication and Applications, Switzerland, Springer, pp. 77–87. doi: https://doi.org/10.1007/978-3-030-02613-4_7.
- Mujeeb, S., N. Javaid, M. Ilahi, Z. Wadud, F. Ishmanov, and M. K. Afzal. 2019. “Deep Long Short-term Memory: A New Price and Load Forecasting Scheme for Big Data in Smart Cities.” Sustainability 11 (4): 987. doi:https://doi.org/10.3390/su11040987.
- Nagbe, K., J. Cugliari, and J. Jacques. 2018. “Short-term Electricity Demand Forecasting Using a Functional State Space Model.” Energies 11 (5): 1120. doi:https://doi.org/10.3390/en11051120.
- Nelson, D. M., A. C. Pereira, and R. A. de Oliveira 2017. “Stock Market’s Price Movement Prediction with LSTM Neural Networks.” 2017 International joint conference on neural networks (IJCNN), Anchorage, AK, USA, May 14–19, 2017, IEEE, pp. 1419–1426. doi: https://doi.org/10.1109/IJCNN.2017.7966019.
- Pan, L., X. Feng, F. Sang, L. Li, M. Leng, and X. Chen. 2019. “An Improved Back Propagation Neural Network Based on Complexity Decomposition Technology and Modified Flower Pollination Optimization for Short-term Load Forecasting.” Neural Computing and Applications 31 (7): 2679–2697. doi:https://doi.org/10.1007/s00521-017-3222-2.
- Papalexopoulos, A. D., and T. C. Hesterberg. 1990. “A Regression-based Approach to Short-term System Load Forecasting.” IEEE Transactions on Power Systems 5 (4): 1535–1547. doi:https://doi.org/10.1109/PICA.1989.39025.
- Park, D. C., El-Sharkawi, M. A., Marks, R. J., Atlas, L., and M. Damborg. 1991. “Electric load forecasting using an artificial neural network.” https://doi.org/10.1109/59.76685.
- Roldán-Blay, C., G. Escrivá-Escrivá, C. Álvarez-Bel, C. Roldán-Porta, and J. Rodríguez-García. 2013. “Upgrade of an Artificial Neural Network Prediction Method for Electrical Consumption Forecasting Using an Hourly Temperature Curve Model.” Energy and Buildings 60: 38–46. doi:https://doi.org/10.1016/j.enbuild.2012.12.009.
- Saeedi, M., M. Moradi, M. Hosseini, A. Emamifar, and N. Ghadimi. 2019. “Robust Optimization Based Optimal Chiller Loading under Cooling Demand Uncertainty.” Applied Thermal Engineering 148: 1081–1091. doi:https://doi.org/10.1016/j.applthermaleng.2018.11.122.
- Sehovac, L., and K. Grolinger. 2020. “Deep Learning for Load Forecasting: Sequence to Sequence Recurrent Neural Networks with Attention.” IEEE Access 8: 36411–36426. doi:https://doi.org/10.1109/ACCESS.2020.2975738.
- Srivastava, N., G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov. 2014. “Dropout: A Simple Way to Prevent Neural Networks from Overfitting.” The Journal of Machine Learning Research 15 (1): 1929–1958.
- Suganthi, L., S. Iniyan, and A. A. Samuel. 2015. “Applications of Fuzzy Logic in Renewable Energy Systems–a Review.” Renewable and Sustainable Energy Reviews 48: 585–607. doi:https://doi.org/10.1016/j.rser.2015.04.037.
- Sutskever, I., O. Vinyals, and Q. V. Le. 2014. “Sequence to Sequence Learning with Neural Networks.” Advances in Neural Information Processing Systems, Montreal, Canada, December 8 – 13, 2014, 3104–3112.
- Szkuta, B., L. A. Sanabria, and T. S. Dillon. 1999. “Electricity Price Short-term Forecasting Using Artificial Neural Networks.” IEEE Transactions on Power Systems 14 (3): 851–857. doi:https://doi.org/10.1109/59.780895.
- Tong, C., J. Li, C. Lang, F. Kong, J. Niu, and J. J. Rodrigues. 2018. “An Efficient Deep Model for Day-ahead Electricity Load Forecasting with Stacked Denoising Auto-encoders.” Journal of Parallel and Distributed Computing 117: 267–273. doi:https://doi.org/10.1016/j.jpdc.2017.06.007.
- Tso, G. K., and K. K. Yau. 2007. “Predicting Electricity Energy Consumption: A Comparison of Regression Analysis, Decision Tree and Neural Networks.” Energy 32 (9): 1761–1768. doi:https://doi.org/10.1016/j.energy.2006.11.010.
- Ugurlu, U., I. Oksuz, and O. Tas. 2018. “Electricity Price Forecasting Using Recurrent Neural Networks.” Energies 11 (5): 1255. doi:https://doi.org/10.3390/en11051255.
- Walker, S., W. Khan, K. Katic, W. Maassen, and W. Zeiler. 2020. “Accuracy of Different Machine Learning Algorithms and Added-value of Predicting Aggregated-level Energy Performance of Commercial Buildings.” Energy and Buildings 209: 109705. doi:https://doi.org/10.1016/j.enbuild.2019.109705.
- Wan, L., M. Zeiler, S. Zhang, Y. Le Cun, and R. Fergus 2013. “Regularization of Neural Networks Using Dropconnect.” International conference on machine learning, Atlanta, USA, June 16 – 21, 2013, pp. 1058–1066.
- Wang, F., K. Li, L. Zhou, H. Ren, J. Contreras, M. Shafie-Khah, and J. P. Catalão. 2019. “Daily Pattern Prediction Based Classification Modeling Approach for Day-ahead Electricity Price Forecasting.” International Journal of Electrical Power & Energy Systems 105: 529–540. doi:https://doi.org/10.1016/j.ijepes.2018.08.039.
- Wang, J., F. Liu, Y. Song, and J. Zhao. 2016. “A Novel Model: Dynamic Choice Artificial Neural Network (DCANN) for an Electricity Price Forecasting System.” Applied Soft Computing 48: 281–297. doi:https://doi.org/10.1016/j.asoc.2016.07.011.
- Wang, K., C. Xu, Y. Zhang, S. Guo, and A. Y. Zomaya. 2017. “Robust Big Data Analytics for Electricity Price Forecasting in the Smart Grid.” IEEE Transactions on Big Data 5 (1): 34–45. doi:https://doi.org/10.1109/TBDATA.2017.2723563.
- Wang, K., X. Qi, and H. Liu. 2019. “Photovoltaic Power Forecasting Based LSTM-Convolutional Network.” Energy 189: 116225. doi:https://doi.org/10.1016/j.energy.2019.116225.
- Xu, L., C. Li, X. Xie, and G. Zhang. 2018. “Long-short-term Memory Network Based Hybrid Model for Short-term Electrical Load Forecasting.” Information 9 (7): 165. doi:https://doi.org/10.3390/info9070165.
- Yu, M., and S. H. Hong. 2016. “Supply–demand Balancing for Power Management in Smart Grid: A Stackelberg Game Approach.” Applied Energy 164: 702–710. doi:https://doi.org/10.1016/j.apenergy.2015.12.039.
- Yue, H., L. Dan, and G. Liqun 2012. “Power System Short-term Load Forecasting Based on Neural Network with Artificial Immune Algorithm.” 2012 24th Chinese Control and Decision Conference (CCDC), Taiyuan, China, May 23–25, 2012, IEEE, pp. 844–848. doi: https://doi.org/10.1109/CCDC.2012.6244131.
- Zahid, M., F. Ahmed, N. Javaid, R. A. Abbasi, H. S. Zainab Kazmi, A. Javaid, M. Bilal, M. Akbar, and M. Ilahi. 2019. “Electricity Price and Load Forecasting Using Enhanced Convolutional Neural Network and Enhanced Support Vector Regression in Smart Grids.” Electronics 8 (2): 122. doi:https://doi.org/10.3390/electronics8020122.
- Zaytar, M. A., and C. El Amrani. 2016. “Sequence to Sequence Weather Forecasting with Long Short-term Memory Recurrent Neural Networks.” International Journal of Computer Applications 143 (11): 7–11. doi:https://doi.org/10.5120/ijca2016910497.
- Zhang, R., Z. Y. Dong, Y. Xu, K. Meng, and K. P. Wong. 2013. “Short-term Load Forecasting of Australian National Electricity Market by an Ensemble Model of Extreme Learniweang Machine.” IET Generation, Transmission & Distribution 7 (4): 391–397. doi:https://doi.org/10.1049/iet-gtd.2012.0541.