Abstract
Electricity is one of the critical role players to build an economy. Electricity consumption and generation can affect the overall policy of the country. Such importance opens an area for intelligent systems that can provide future insights. Intelligent management for electric power consumption requires future electricity power consumption prediction with less error. These predictions provide insights for making decisions to smooth line the policy and grow the country’s economy. Future prediction can be categorized into three categories, namely (1) Long-Term, (2) Short-Term, and (3) Mid-Term predictions. For our study, we consider the Mid-Term electricity consumption prediction. Dataset provided by Korea Electric power supply to get insights for a metropolitan city like Seoul. Dataset is in time-series, so statistical and machine learning models can be used. This study provides experimental results from the proposed ARIMA and CNN-Bi-LSTM. Hyperparameters are tuned for ARIMA and neural network models to increase the models’ accuracy, which looks promising as RMSE for training is 0.14 and 0.20 RMSE for testing.
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References
Siano P (2014) Demand response and smart grids—a survey. Renew Sustain Energy Rev. https://doi.org/10.1016/j.rser.2013.10.022
Ardakani FJ, Ardehali MM (2014) Long-term electrical energy consumption forecasting for developing and developed economies based on different optimized models and historical data types. Energy. https://doi.org/10.1016/j.energy.2013.12.031
Chatzis SP, Siakoulis V, Petropoulos A, Stavroulakis E, Vlachogiannakis N (2018) Forecasting stock market crisis events using deep and statistical machine learning techniques. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2018.06.032
Long HV, Son LH, Khari M, Arora K, Chopra S, Kumar R, Le T, Baik SW (2019) A new approach for construction of geodemographic segmentation model and prediction analysis. Comput Intell Neurosci. https://doi.org/10.1155/2019/9252837
Kavasseri RG, Seetharaman K (2009) Day-ahead wind speed forecasting using f-ARIMA models. Renew Energy. https://doi.org/10.1016/j.renene.2008.09.006
Fan S, Hyndman RJ (2012) Short-term load forecasting based on a semi-parametric additive model. IEEE Trans Power Syst. https://doi.org/10.1109/TPWRS.2011.2162082
Kaytez F, Taplamacioglu MC, Cam E, Hardalac F (2015) Forecasting electricity consumption: a comparison of regression analysis, neural networks and least squares support vector machines. Int J Electr Power Energy Syst. https://doi.org/10.1016/j.ijepes.2014.12.036
Ha S, Tae S, Kim R (2019) Energy demand forecast models for commercial buildings in South Korea. Energies. https://doi.org/10.3390/en12122313
Shinde P, Literature Amelin MA (2019) Review of intraday electricity markets and prices. IEEE Milan PowerTech. https://doi.org/10.1109/PTC.2019.8810752
Masood MA, Abid S (2018) Forecasting wheat production using time series models in Pakistan. Asian J Agric Rural Dev. https://doi.org/10.18488/journal.1005/2018.8.2/1005.2.172.177
Mishra AK, Sahanaa C, Manikandan M (2019) Forecasting Indian infant mortality rate: an application of autoregressive integrated moving average model. J Family Community Med. https://doi.org/10.4103/jfcm.JFCM_51_18
Amin P, Cherkasova L, Aitken R, Kache V (2019) Automating energy demand modeling and forecasting using smart meter data. In: Proceedings—2019 IEEE International Congress on Internet Of Things, ICIOT 2019—Part of the 2019 IEEE World Congress on Services. https://doi.org/10.1109/ICIOT.2019.00032
Debnath KB, Mourshed M (2018) Forecasting methods in energy planning models. Renew Sustain Energy Rev. https://doi.org/10.1016/j.rser.2018.02.002
Author Information Pack (2018) Adv. Account. https://doi.org/10.1016/s0882-6110(18)30184-6
Ediger VŞ, Akar S, Uǧurlu B (2006) Forecasting production of fossil fuel sources in turkey using a comparative regression and ARIMA model. Energy Policy. https://doi.org/10.1016/j.enpol.2005.08.023
de Oliveira EM, Cyrino Oliveira FL (2018) Forecasting mid-long term electric energy consumption through bagging ARIMA and exponential smoothing methods. Energy. https://doi.org/10.1016/j.energy.2017.12.049
Romero-Gelvez JI, Delgado-Sierra EA, Herrera-Cuartas JA, Garcia-Bedoya O (2019) Demand forecasting and material requirement planning optimization using open source tools. In: CEUR workshop proceedings
Shin HC, Roth HR, Gao M, Lu L, Xu Z, Nogues I, Yao J, Mollura D, Summers RM (2016) Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging. https://doi.org/10.1109/TMI.2016.2528162
Saeed F, Paul A, Hong WH, Seo H (2020) Machine learning based approach for multimedia surveillance during fire emergencies. Multimed Tools Appl. https://doi.org/10.1007/s11042-019-7548-x
Read SJ, Droutman V, Smith BJ, Miller LC (2019) Using neural networks as models of personality process: a tutorial. Pers Individ Differ. https://doi.org/10.1016/j.paid.2017.11.015
Abdel-Nasser M, Mahmoud K (2019) Accurate photovoltaic power forecasting models using deep LSTM-RNN. Neural Comput Appl. https://doi.org/10.1007/s00521-017-3225-z
Bouktif S, Fiaz A, Ouni A, Serhani MA (2018) Optimal deep learning LSTM model for electric load forecasting using feature selection and genetic algorithm: comparison with machine learning approaches. Energies. https://doi.org/10.3390/en11071636
Le T, Vo MT, Vo B, Hwang E, Rho S, Baik SW (2019) Improving electric energy consumption prediction using CNN and Bi-LSTM. Appl Sci. https://doi.org/10.3390/app9204237
Kong W, Dong ZY, Jia Y, Hill DJ, Xu Y, Zhang Y (2019) Short-term residential load forecasting based on LSTM recurrent neural network. IEEE Trans Smart Grid. https://doi.org/10.1109/TSG.2017.2753802
Pessanha JFM, Leon N (2015) Forecasting long-term electricity demand in the residential sector. Proc Comput Sci. https://doi.org/10.1016/j.procs.2015.07.032
Rodrigues F, Cardeira C, Calado JMF (2014) The daily and hourly energy consumption and load forecasting using artificial neural network method: a case study using a set of 93 households in Portugal. Energy Proc. https://doi.org/10.1016/j.egypro.2014.12.383
Lai G, Chang WC, Yang Y, Liu H (2018) Modeling long- and short-term temporal patterns with deep neural networks. In: 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018. https://doi.org/10.1145/3209978.3210006
Kourentzes N, Barrow DK, Crone SF (2014) Neural network ensemble operators for time series forecasting. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2013.12.011
West D (2000) Neural network credit scoring models. Oper Res. https://doi.org/10.1016/S0305-0548(99)00149-5
McDermott PL, Wikle CK (2019) Bayesian recurrent neural network models for forecasting and quantifying uncertainty in spatial-temporal data. Entropy. https://doi.org/10.3390/e21020184
Majidpour M, Nazaripouya H, Chu P, Pota H, Gadh R (2018) Fast univariate time series prediction of solar power for real-time control of energy storage system. Forecasting. https://doi.org/10.3390/forecast1010008
Acknowledgements
This work was supported by the faculty research fund of Sejong University in 2020 and also supported by Energy Cloud R&D Program(Grant No. 2019M3F2A1073184) through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT.
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Gul, M.J., Urfa, G.M., Paul, A. et al. Mid-term electricity load prediction using CNN and Bi-LSTM. J Supercomput 77, 10942–10958 (2021). https://doi.org/10.1007/s11227-021-03686-8
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DOI: https://doi.org/10.1007/s11227-021-03686-8