Exploiting Deep Learning for Wind Power Forecasting Based on Big Data Analytics
Recently, power systems are facing the challenges of growing power demand, depleting fossil fuel and aggravating environmental pollution (caused by carbon emission from fossil fuel based power generation). The incorporation of alternative low carbon energy generation, i.e., Renewable Energy Sources...
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| Published in: | Applied sciences Vol. 9; no. 20; p. 4417 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
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MDPI AG
01.10.2019
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| ISSN: | 2076-3417, 2076-3417 |
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| Abstract | Recently, power systems are facing the challenges of growing power demand, depleting fossil fuel and aggravating environmental pollution (caused by carbon emission from fossil fuel based power generation). The incorporation of alternative low carbon energy generation, i.e., Renewable Energy Sources (RESs), becomes crucial for energy systems. Effective Demand Side Management (DSM) and RES incorporation enable power systems to maintain demand, supply balance and optimize energy in an environmentally friendly manner. The wind power is a popular energy source because of its environmental and economical benefits. However, the uncertainty of wind power makes its incorporation in energy systems really difficult. To mitigate the risk of demand-supply imbalance, an accurate estimation of wind power is essential. Recognizing this challenging task, an efficient deep learning based prediction model is proposed for wind power forecasting. The proposed model has two stages. In the first stage, Wavelet Packet Transform (WPT) is used to decompose the past wind power signals. Other than decomposed signals and lagged wind power, multiple exogenous inputs (such as, calendar variable and Numerical Weather Prediction (NWP)) are also used as input to forecast wind power. In the second stage, a new prediction model, Efficient Deep Convolution Neural Network (EDCNN), is employed to forecast wind power. A DSM scheme is formulated based on forecasted wind power, day-ahead demand and price. The proposed forecasting model’s performance was evaluated on big data of Maine wind farm ISO NE, USA. |
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| AbstractList | Recently, power systems are facing the challenges of growing power demand, depleting fossil fuel and aggravating environmental pollution (caused by carbon emission from fossil fuel based power generation). The incorporation of alternative low carbon energy generation, i.e., Renewable Energy Sources (RESs), becomes crucial for energy systems. Effective Demand Side Management (DSM) and RES incorporation enable power systems to maintain demand, supply balance and optimize energy in an environmentally friendly manner. The wind power is a popular energy source because of its environmental and economical benefits. However, the uncertainty of wind power makes its incorporation in energy systems really difficult. To mitigate the risk of demand-supply imbalance, an accurate estimation of wind power is essential. Recognizing this challenging task, an efficient deep learning based prediction model is proposed for wind power forecasting. The proposed model has two stages. In the first stage, Wavelet Packet Transform (WPT) is used to decompose the past wind power signals. Other than decomposed signals and lagged wind power, multiple exogenous inputs (such as, calendar variable and Numerical Weather Prediction (NWP)) are also used as input to forecast wind power. In the second stage, a new prediction model, Efficient Deep Convolution Neural Network (EDCNN), is employed to forecast wind power. A DSM scheme is formulated based on forecasted wind power, day-ahead demand and price. The proposed forecasting model’s performance was evaluated on big data of Maine wind farm ISO NE, USA. |
| Author | Mujeeb, Sana Saba, Tanzila Alghamdi, Turki Ali Javaid, Nadeem Fatima, Aisha Ullah, Sameeh |
| Author_xml | – sequence: 1 givenname: Sana surname: Mujeeb fullname: Mujeeb, Sana – sequence: 2 givenname: Turki Ali orcidid: 0000-0001-6706-2183 surname: Alghamdi fullname: Alghamdi, Turki Ali – sequence: 3 givenname: Sameeh surname: Ullah fullname: Ullah, Sameeh – sequence: 4 givenname: Aisha orcidid: 0000-0001-9620-4806 surname: Fatima fullname: Fatima, Aisha – sequence: 5 givenname: Nadeem surname: Javaid fullname: Javaid, Nadeem – sequence: 6 givenname: Tanzila orcidid: 0000-0003-3138-3801 surname: Saba fullname: Saba, Tanzila |
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| Cites_doi | 10.1109/TSTE.2015.2429586 10.1063/1.5024297 10.3390/s18113947 10.1109/TSG.2014.2377292 10.1063/1.4995334 10.1002/we.1779 10.1155/2018/9327536 10.1186/s40537-014-0007-7 10.1007/978-3-030-33509-0_5 10.1109/TSTE.2018.2831238 10.1063/1.5034022 10.1109/TSTE.2016.2604679 10.1016/j.asoc.2017.05.031 10.1109/PCT.2007.4538398 10.1016/j.apenergy.2016.05.083 10.1016/j.apenergy.2018.02.069 10.1007/s11367-016-1075-z 10.1016/j.energy.2015.12.026 10.1016/j.energy.2017.01.104 10.1109/TSTE.2016.2560628 10.3390/en7074185 10.1109/TSG.2013.2280649 10.1016/j.scs.2019.101642 10.3390/electronics8020122 10.1016/j.swevo.2011.02.002 10.1016/j.apenergy.2016.11.111 10.1109/TSTE.2015.2441747 10.1016/j.rser.2015.11.050 10.1109/TSTE.2018.2841938 10.1109/JIOT.2017.2677578 10.1002/we.2029 10.1109/TPWRS.2018.2794450 10.3390/su11040987 10.1007/s40565-013-0012-4 10.1016/j.inffus.2017.10.006 10.1109/TSTE.2017.2718518 10.1016/j.renene.2014.09.060 10.1016/j.enconman.2014.10.001 10.1080/07350015.1995.10524599 10.1109/TSTE.2015.2472963 10.1016/j.apenergy.2016.03.096 10.1109/18.119732 10.1109/TSTE.2019.2890875 10.1109/TPWRS.2014.2299801 10.1109/TSTE.2018.2820198 10.1016/j.apenergy.2015.08.040 10.1109/TPWRS.2014.2363873 |
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| References | Shao (ref_34) 2018; 10 Lago (ref_52) 2018; 221 Zhang (ref_16) 2017; 122 ref_13 Chitsaz (ref_31) 2015; 89 Zhao (ref_48) 2018; 33 Tomporowski (ref_6) 2017; 19 ref_19 Chen (ref_11) 2013; 1 Chen (ref_53) 2014; 7 Xu (ref_20) 2015; 6 Zhao (ref_1) 2016; 177 Stephens (ref_38) 2015; 6 Torres (ref_35) 2018; 10 Cavalcante (ref_24) 2017; 20 Zhang (ref_17) 2018; 42 ref_22 Torres (ref_37) 2018; 2018 Yang (ref_23) 2018; 10 Pascual (ref_40) 2015; 158 Haque (ref_12) 2014; 29 Ghasemi (ref_39) 2016; 177 Wang (ref_47) 2018; 10 Derrac (ref_50) 2011; 1 Najafabadi (ref_18) 2015; 2 Wang (ref_9) 2016; 7 Diebold (ref_51) 1995; 13 Lin (ref_26) 2019; 10 Haupt (ref_15) 2016; 8 Yan (ref_25) 2018; 10 Coifman (ref_43) 1992; 38 Wu (ref_32) 2017; 4 Wang (ref_36) 2017; 188 ref_46 ref_45 ref_44 ref_41 Lee (ref_30) 2014; 5 Zhou (ref_14) 2016; 56 ref_2 Li (ref_29) 2015; 6 ref_49 Zhang (ref_21) 2015; 30 Qureshi (ref_33) 2017; 58 Ellis (ref_27) 2015; 18 Swinand (ref_10) 2015; 75 Athari (ref_8) 2018; 9 Yan (ref_28) 2016; 7 Jong (ref_3) 2016; 100 Shafiee (ref_5) 2016; 21 Mujeeb (ref_42) 2019; 51 ref_4 ref_7 |
| References_xml | – volume: 6 start-page: 1283 year: 2015 ident: ref_20 article-title: A short-term wind power forecasting approach with adjustment of numerical weather prediction input by data mining publication-title: IEEE Trans. Sustain. Energy doi: 10.1109/TSTE.2015.2429586 – volume: 10 start-page: 43303 year: 2018 ident: ref_34 article-title: A novel deep learning approach for short-term wind power forecasting based on infinite feature selection and recurrent neural network publication-title: J. Renew. Sustain. Energy doi: 10.1063/1.5024297 – ident: ref_49 doi: 10.3390/s18113947 – volume: 6 start-page: 1394 year: 2015 ident: ref_38 article-title: Game theoretic model predictive control for distributed energy demand-side management publication-title: IEEE Trans. Smart Grid doi: 10.1109/TSG.2014.2377292 – volume: 10 start-page: 013305 year: 2018 ident: ref_35 article-title: Deep learning to predict the generation of a wind farm publication-title: J. Renew. Sustain. Energy doi: 10.1063/1.4995334 – volume: 18 start-page: 1611 year: 2015 ident: ref_27 article-title: Predicting wind power variability events using different statistical methods driven by regional atmospheric model output publication-title: Wind Energy doi: 10.1002/we.1779 – volume: 2018 start-page: 9327536 year: 2018 ident: ref_37 article-title: Using deep learning to predict complex systems: A case study in wind farm generation publication-title: Complexity doi: 10.1155/2018/9327536 – volume: 2 start-page: 1 year: 2015 ident: ref_18 article-title: Deep learning applications and challenges in big data analytics publication-title: J. Big Data doi: 10.1186/s40537-014-0007-7 – ident: ref_19 doi: 10.1007/978-3-030-33509-0_5 – volume: 10 start-page: 226 year: 2019 ident: ref_26 article-title: A multi-model combination approach for probabilistic wind power forecasting publication-title: IEEE Trans. Sustain. Energy doi: 10.1109/TSTE.2018.2831238 – ident: ref_4 – volume: 10 start-page: 45908 year: 2018 ident: ref_23 article-title: The efficient market operation for wind energy trading based on the dynamic improved power forecasting publication-title: J. Renew. Sustain. Energy doi: 10.1063/1.5034022 – volume: 8 start-page: 725 year: 2016 ident: ref_15 article-title: Variable generation power forecasting as a big data problem publication-title: IEEE Trans. Sustain. Energy doi: 10.1109/TSTE.2016.2604679 – volume: 58 start-page: 742 year: 2017 ident: ref_33 article-title: Wind power prediction using deep neural network based meta regression and transfer learning publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2017.05.031 – ident: ref_13 doi: 10.1109/PCT.2007.4538398 – volume: 177 start-page: 40 year: 2016 ident: ref_39 article-title: A novel hybrid algorithm for electricity price and load forecasting in smart grids with demand-side management publication-title: Appl. Energy doi: 10.1016/j.apenergy.2016.05.083 – volume: 221 start-page: 386 year: 2018 ident: ref_52 article-title: Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms publication-title: Appl. Energy doi: 10.1016/j.apenergy.2018.02.069 – volume: 21 start-page: 961 year: 2016 ident: ref_5 article-title: A parametric whole life cost model for offshore wind farms publication-title: Int. J. Life Cycle Assess. doi: 10.1007/s11367-016-1075-z – volume: 100 start-page: 401 year: 2016 ident: ref_3 article-title: Integrating large scale wind power into the electricity grid in the Northeast of Brazil publication-title: Energy doi: 10.1016/j.energy.2015.12.026 – volume: 122 start-page: 528 year: 2017 ident: ref_16 article-title: Ramp forecasting performance from improved short-term wind power forecasting over multiple spatial and temporal scales publication-title: Energy doi: 10.1016/j.energy.2017.01.104 – ident: ref_7 – volume: 7 start-page: 1525 year: 2016 ident: ref_9 article-title: Quantifying the economic and grid reliability impacts of improved wind power forecasting publication-title: IEEE Trans. Sustain. Energy doi: 10.1109/TSTE.2016.2560628 – volume: 7 start-page: 4185 year: 2014 ident: ref_53 article-title: Refined Diebold-Mariano test methods for the evaluation of wind power forecasting models publication-title: Energies doi: 10.3390/en7074185 – volume: 5 start-page: 501 year: 2014 ident: ref_30 article-title: Short-term wind power ensemble prediction based on Gaussian processes and neural networks publication-title: IEEE Trans. Smart Grid doi: 10.1109/TSG.2013.2280649 – volume: 51 start-page: 101642 year: 2019 ident: ref_42 article-title: ESAENARX and DE-RELM: Novel schemes for big data predictive analytics of electricity load and price publication-title: Sustain. Cities Soc. doi: 10.1016/j.scs.2019.101642 – ident: ref_45 doi: 10.3390/electronics8020122 – volume: 1 start-page: 3 year: 2011 ident: ref_50 article-title: A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms publication-title: Swarm Evol. Comput. doi: 10.1016/j.swevo.2011.02.002 – volume: 188 start-page: 56 year: 2017 ident: ref_36 article-title: Deep learning based ensemble approach for probabilistic wind power forecasting publication-title: Appl. Energy doi: 10.1016/j.apenergy.2016.11.111 – volume: 6 start-page: 1447 year: 2015 ident: ref_29 article-title: Wind power forecasting using neural network ensembles with feature selection publication-title: IEEE Trans. Sustain Energy doi: 10.1109/TSTE.2015.2441747 – volume: 56 start-page: 215 year: 2016 ident: ref_14 article-title: Big data driven smart energy management: From big data to big insights publication-title: Renew. Sustain. Energy Rev. doi: 10.1016/j.rser.2015.11.050 – volume: 10 start-page: 625 year: 2018 ident: ref_25 article-title: Analytical Iterative Multistep Interval Forecasts of Wind Generation Based on TLGP publication-title: IEEE Trans. Sustain. Energy doi: 10.1109/TSTE.2018.2841938 – volume: 4 start-page: 979 year: 2017 ident: ref_32 article-title: A Data Mining Approach Combining K-Means Clustering With Bagging Neural Network for Short-Term Wind Power Forecasting publication-title: IEEE Internet Things J. doi: 10.1109/JIOT.2017.2677578 – volume: 20 start-page: 657 year: 2017 ident: ref_24 article-title: LASSO vector autoregression structures for very short-term wind power forecasting publication-title: Wind Energy doi: 10.1002/we.2029 – volume: 33 start-page: 5029 year: 2018 ident: ref_48 article-title: Correlation-constrained and sparsity-controlled vector autoregressive model for spatio-temporal wind power forecasting publication-title: IEEE Trans. Power Syst. doi: 10.1109/TPWRS.2018.2794450 – ident: ref_41 doi: 10.3390/su11040987 – volume: 1 start-page: 2 year: 2013 ident: ref_11 article-title: Wind power in modern power systems publication-title: J. Mod. Power Syst. Clean Energy doi: 10.1007/s40565-013-0012-4 – ident: ref_44 – volume: 42 start-page: 146 year: 2018 ident: ref_17 article-title: A survey on deep learning for big data publication-title: Inf. Fusion doi: 10.1016/j.inffus.2017.10.006 – volume: 19 start-page: 694 year: 2017 ident: ref_6 article-title: Environmental control of wind power technology publication-title: Rocznik Ochrona Srodowiska – volume: 9 start-page: 128 year: 2018 ident: ref_8 article-title: Impacts of Wind Power Uncertainty on Grid Vulnerability to Cascading Overload Failures publication-title: IEEE Trans. Sustain. Energy doi: 10.1109/TSTE.2017.2718518 – volume: 75 start-page: 468 year: 2015 ident: ref_10 article-title: Estimating the impact of wind generation and wind forecast errors on energy prices and costs in Ireland publication-title: Renew. Energy doi: 10.1016/j.renene.2014.09.060 – volume: 89 start-page: 588 year: 2015 ident: ref_31 article-title: Wind power forecast using wavelet neural network trained by improved Clonal selection algorithm publication-title: Energy Convers. Manag. doi: 10.1016/j.enconman.2014.10.001 – volume: 13 start-page: 253 year: 1995 ident: ref_51 article-title: Comparing predictive accuracy publication-title: J. Bus. Econ. Stat. doi: 10.1080/07350015.1995.10524599 – ident: ref_2 – volume: 7 start-page: 87 year: 2016 ident: ref_28 article-title: Hybrid probabilistic wind power forecasting using temporally local Gaussian process publication-title: IEEE Trans. Sustain. Energy doi: 10.1109/TSTE.2015.2472963 – ident: ref_46 – volume: 177 start-page: 793 year: 2016 ident: ref_1 article-title: A novel bidirectional mechanism based on time series model for wind power forecasting publication-title: Appl. Energy doi: 10.1016/j.apenergy.2016.03.096 – volume: 38 start-page: 713 year: 1992 ident: ref_43 article-title: Entropy-based algorithms for best basis selection publication-title: IEEE Trans. Inf. Theory doi: 10.1109/18.119732 – ident: ref_22 doi: 10.1109/TSTE.2019.2890875 – volume: 29 start-page: 1663 year: 2014 ident: ref_12 article-title: A hybrid intelligent model for deterministic and quantile regression approach for probabilistic wind power forecasting publication-title: IEEE Trans. Power Syst. doi: 10.1109/TPWRS.2014.2299801 – volume: 10 start-page: 16 year: 2018 ident: ref_47 article-title: Wind power curve modeling and wind power forecasting with inconsistent data publication-title: IEEE Trans. Sustain. Energy doi: 10.1109/TSTE.2018.2820198 – volume: 158 start-page: 12 year: 2015 ident: ref_40 article-title: Energy management strategy for a renewable-based residential microgrid with generation and demand forecasting publication-title: Appl. Energy doi: 10.1016/j.apenergy.2015.08.040 – volume: 30 start-page: 2706 year: 2015 ident: ref_21 article-title: An advanced approach for construction of optimal wind power prediction intervals publication-title: IEEE Trans. Power Syst. doi: 10.1109/TPWRS.2014.2363873 |
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| SubjectTerms | Accuracy Alternative energy sources Big Data convolution neural network Data analysis data analytics Decomposition Deep learning demand side management energy management Energy resources Forecasting Forecasting techniques Industrial plant emissions Neural networks Optimization techniques Power supply Random variables Renewable resources Teaching methods Time series Wavelet transforms Wind power |
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| Title | Exploiting Deep Learning for Wind Power Forecasting Based on Big Data Analytics |
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| Volume | 9 |
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