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Electrical Engineering
Erschienen in: Electrical Engineering 1/2020

25.11.2019 | Original Paper

A hybrid short-term load forecasting model developed by factor and feature selection algorithms using improved grasshopper optimization algorithm and principal component analysis

verfasst von: Mesbaholdin Salami, Farzad Movahedi Sobhani, Mohammad Sadegh Ghazizadeh

Erschienen in: Electrical Engineering | Ausgabe 1/2020

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Abstract

Hybrid load forecasting models analyze linear and nonlinear components separately. If hybrid models were integrated with factor and feature selection algorithms, they would improve significantly. In the hybrid model proposed by this paper, the initial data were decomposed by an empirical mode decomposition (EMD) model. The linear component was analyzed through the autoregressive integrated moving average (ARIMA) method and the nonlinear component by a neural network (NN) and weighted by the improved flower pollination algorithm (IFPA). With the nonlinear component, the input load demand variable was decomposed by a wavelet transform (WT). In this paper, the improved grasshopper optimization algorithm (IGOA) and the principal component analysis (PCA) were employed to determine the input feature and input factor, respectively. Therefore, the proposed model was composed of EMD, IGOA, PCA, ARIMA, IFPA, NN, and WT algorithms. Finally, Iran’s Electricity Market (IEM) data were used to show improvements in the precision of the proposed forecasting model.
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Literatur
1.
Cadenas E, Rivera W (2007) Wind speed forecasting in the South Coast of Oaxaca, México. Renewable Energy 32(12):2116–2128CrossRef
2.
Pao H-T, Hsin-Chia F, Tseng C-L (2012) Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey model. Energy 40(1):400–409CrossRef
3.
Gonzales Chavez S, Xiberta Bernat J, Llaneza Coalla H (1999) Forecasting of energy production and consumption in Asturias (northern Spain). Energy 24(3):183–198CrossRef
4.
Liu L-M, Lin M-W (1991) Forecasting residential consumption of natural gas using monthly and quarterly time series. Int J Forecast 7(1):3–16CrossRef
5.
Bowden N, Payne JE (2008) Short term forecasting of electricity prices for MISO hubs: evidence from ARIMA-EGARCH models. Energy Econ 30(6):3186–3197CrossRef
6.
Reikard G (2009) Predicting solar radiation at high resolutions: a comparison of time series forecasts. Sol Energy 83(3):342–349CrossRef
7.
Debnath KB, Mourshed M (2018) Forecasting methods in energy planning models. Renew Sustain Energy Rev 88:297–325CrossRef
8.
Xin F, Zeng X-J, Feng P, Cai X (2018) Clustering-based short-term load forecasting for residential electricity under the increasing-block pricing tariffs in China. Energy 165(Part B):76–89
9.
Sepasi S, Reihani E, Howlader AM, Roose LR, Matsuura MM (2017) Very short term load forecasting of a distribution system with high PV penetration. Renew Energy 106:142–148CrossRef
10.
Ahmad T, Chen H (2018) Short and medium-term forecasting of cooling and heating load demand in building environment with data-mining based approaches. Energy Build 166:460–476CrossRef
11.
De Felice M, Alessandri A, Catalano F (2015) Seasonal climate forecasts for medium-term electricity demand forecasting. Appl Energy 137:435–444CrossRef
12.
Ali D, Yohanna m, Puwu MI, Garkida BM (2016) Long-term load forecast modelling using a fuzzy logic approach. Pac Sci Rev A Nat Sci Eng 18(2):123–127
13.
Dudek G (2016) Pattern-based local linear regression models for short-term load forecasting. Electr Power Syst Res 130:139–147CrossRef
14.
Lee C-M, Ko C-N (2011) Short-term load forecasting using lifting scheme and ARIMA models. Expert Syst Appl 38(5):5902–5911CrossRef
15.
Elamin N, Fukushige M (2018) Modeling and forecasting hourly electricity demand by SARMAX with interactions. Energy 165(Part B):257–268CrossRef
16.
Tarsitano A, Amerise IL (2017) Short-term load forecasting using a two-stage SARMAX model. Energy 133:108–114CrossRef
17.
Zhang M, Bao H, Yan L, Cao JP, Jian-Guang DU (2003) Research on processing of short-term historical data of daily load based on Kalman filter. Power Syst Technol 10:e1–e9
18.
Chen C-C, Wang Y-R, Guo Y-LL, Wang Y-C, Mong-Ming L (2019) Short-term prediction of extremely hot days in summer due to climate change and ENSO and related attributable mortality. Sci Total Environ 661:10–11CrossRef
19.
Kouhi S, Keynia F (2013) A new cascade NN based method to short-term load forecast in deregulated electricity market. Energy Convers Manag 71:e76–e83CrossRef
20.
Pai PF, Hong WC (2005) Forecasting regional electricity load based on recurrent support vector machines with genetic algorithms. Electr Power Syst Res 74:e417–e425CrossRef
21.
Yang YL, Che JX, Li YY, Zhao YJ, Zhu SL (2017) An incremental electric load forecasting model based on support vector regression. Energy 113:e796–e808CrossRef
22.
Chenthur Pandian S, Duraiswamy K, Christober Asir Rajan C, Kanagaraj N (2006) Fuzzy approach for short term load forecasting. Electr Power Syst Res 76:e541–e548CrossRef
23.
Pai P-F, Hong W-C (2005) Support vector machines with simulated annealing algorithms in electricity load forecasting. Energy Convers Manag 46(17):2669–2688CrossRef
24.
Kavousi-Fard A, Samet H, Marzbani F (2014) A new hybrid modified firefly algorithm and support vector regression model for accurate short term load forecasting. Expert Syst Appl 41(13):6047–6056CrossRef
25.
Irani R, Nasimi R (2011) Evolving neural network using real coded genetic algorithm for permeability estimation of the reservoir. Expert Syst Appl 38(8):9862–9866CrossRef
26.
Zhang JR, Zhang J, Lok TM, Lyu MR (2007) A hybrid particle swarm optimization-back-propagation algorithm for feedforward neural network training. Appl Math Comput 185(2):1026–1037MATH
27.
Wang L, Zeng Y, Chen T (2015) Back propagation neural network with adaptive differential evolution algorithm for time series forecasting. Expert Syst Appl 42(2):855–863CrossRef
28.
29.
30.
Sadaei HJ, Silva PCL, Guimarães FG, Lee HM (2019) Short-term load forecasting by using a combined method of convolutional neural networks and fuzzy time series. Energy 175:365–377CrossRef
31.
Wang S, Wang X, Wang S, Wang D (2019) Bi-directional long short-term memory method based on attention mechanism and rolling update for short-term load forecasting. Int J Electr Power Energy Syst 109:470–479CrossRef
32.
Hongfang L, Azimi M, Iseley T (2019) Short-term load forecasting of urban gas using a hybrid model based on improved fruit fly optimization algorithm and support vector machine. Energy Rep 5:666–677CrossRef
33.
He F, Zhou J, Feng Z, Liu Guangbiao, Yang Y (2019) A hybrid short-term load forecasting model based on variational mode decomposition and long short-term memory networks considering relevant factors with Bayesian optimization algorithm. Appl Energy 237:103–116CrossRef
34.
Zhang J, Wei Y-M, Li D, Tan Z, Zhou J (2018) Short term electricity load forecasting using a hybrid model. Energy 158:774–781CrossRef
35.
Yang A, Li W, Yang X (2019) Short-term electricity load forecasting based on feature selection and least squares support vector machines. Knowl Based Syst 163:159–173CrossRef
36.
Ghadimi N, Akbarimajd A, Shayeghi H, Abedinia O (2018) Two stage forecast engine with feature selection technique and improved meta-heuristic algorithm for electricity load forecasting. Energy 161:130–142CrossRef
37.
Jiang H (2018) Model forecasting based on two-stage feature selection procedure using orthogonal greedy algorithm. Appl Soft Comput 63:110–123CrossRef
38.
Jiang P, Liu F, Song Y (2017) A hybrid forecasting model based on date-framework strategy and improved feature selection technology for short-term load forecasting. Energy 119:694–709CrossRef
39.
Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimisation algorithm: theory and application. Adv Eng Softw 105:30–47CrossRef
40.
Luo J, Chen H, Zhang Q, Xu Y, Huang H, Zhao X (2018) An improved grasshopper optimization algorithm with application to financial stress prediction. Appl Math Model 64:654–668MathSciNetCrossRef
41.
Dong Y, Qin SJ (2018) A novel dynamic PCA algorithm for dynamic data modeling and process monitoring. J Process Control 67:1–11CrossRef
42.
Mallat S (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11(7):674–693MATHCrossRef
43.
Conejo A, Plazas M, Espinola R, Molina A (2005) Day-ahead electricity price forecasting using wavelet transform and ARIMA models. IEEE Trans Power Syst 20(2):1035–1042CrossRef
44.
Reis A, da Silva A (2005) Feature extraction via multiresolution analysis for short term load forecasting. IEEE Trans Power Syst 20(1):189–198CrossRef
45.
Yang X-S (2012) Flower pollination algorithm for global optimization. In: UCNC 2012: unconventional computation and natural computation, pp 240–249
46.
Gao M, Shen J, Jiang J (2018) Visual tracking using improved flower pollination algorithm. Optik 156:522–529CrossRef
47.
Willmott CJ (1982) Some comments on the evaluation of model performance. Bull Am Meteorol Soc 63:1309–1313CrossRef
48.
Willmott CJ (1981) On the validation of models. Phys Geogr 2:184–194CrossRef
49.
Dawson CW, Abrahart RJ, See LM (2007) HydroTest: a web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts. Environ Modell Softw 22:1034–1052CrossRef
50.
51.
Ingrand P (2018) Le test t de student. Journal d’imagerie Diagnostique et Interventionnelle 1(2):81–83CrossRef
Metadaten
Titel
A hybrid short-term load forecasting model developed by factor and feature selection algorithms using improved grasshopper optimization algorithm and principal component analysis
verfasst von
Mesbaholdin Salami
Farzad Movahedi Sobhani
Mohammad Sadegh Ghazizadeh
Publikationsdatum
25.11.2019
Verlag
Springer Berlin Heidelberg
Erschienen in
Electrical Engineering / Ausgabe 1/2020
Print ISSN: 0948-7921
Elektronische ISSN: 1432-0487
DOI
https://doi.org/10.1007/s00202-019-00886-7

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