Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Energy Systems

08-04-2023 | Review Article

Data analytics in the electricity market: a systematic literature review

Authors: Mahmood Hosseini Imani, Ettore Bompard, Pietro Colella, Tao Huang

Published in: Energy Systems

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

In the last decade, data analytics studies have covered a wide range of fields across the entire value chain in the electricity sector, from production and transmission to the electricity market, distribution, and load consumption. It is essential to integrate and organize the wide range of current scientific publications to effectively allow researchers and specialists to implement and progress cutting-edge methodologies in the future. Because of the electricity market’s significance in the value chain of the electricity sector, in this study, we structure a systematic literature review of the data analytics-related works following the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) framework to categorize the more common applications and approaches in the electricity market field. After refining the identified studies from the Web of Science database using the inclusion and exclusion criteria, 925 articles were chosen as the final pool of literature. Investigation of the extracted studies reveals that the application of data analytics in the electricity market can be clustered into four distinct groups: Prediction, Demand Side Management (DSM), Analysis of the market power, and Market simulation. Within the categorized applications, Prediction with 67% is the most frequent application of data analytics in the electricity market, followed by market simulation (14%), analysis of the market power (9%), DSM (7%), and other applications (3%).

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now
Literature
1.
Lian, D., Li Da, X.: Data analytics in industry 4.0: A survey. Inform. Syst. Front, 1–17 (2021)
2.
Reza, S., Timos, S.: Prescriptive analytics for big data. In: Australasian database conference, pages 245–256. Springer (2016)
3.
vom Scheidt, F., Medinová, H., Ludwig, N., Richter, B., Staudt, P., Weinhardt, Christof: Data analytics in the electricity sector-a quantitative and qualitative literature review. Energy AI 1, 100009 (2020)CrossRef
4.
Guido, B., Davide, C., Marco L., Della V., Tullio, F.: Electric load management approaches for peak load reduction: a systematic literature review and state of the art. Sustain. Cities Soc. 20, 124–141 (2016)
5.
Atika, Q., Fayaz, H., Ali W., Ram Gopal, R., Rahim, N.A., Waleed Ahmed, K.: The artificial neural network for solar radiation prediction and designing solar systems: a systematic literature review. J. Clean. Product. 104:1–12 (2015)
6.
Weitzel, T., Christoph, G.H.: Energy management for stationary electric energy storage systems: a systematic literature review. Eur. J. Oper. Res. 264(2), 582–606 (2018)MathSciNetMATHCrossRef
7.
Umar, F., Bass, R.B.: Frequency Event detection and mitigation in power systems: a systematic literature review. IEEE Access 10 (2022)
8.
Fonseca, J.D., Camargo, M., Commenge, J.-M., Falk, L., Gil, I.D.: Trends in design of distributed energy systems using hydrogen as energy vector: a systematic literature review. Int. J. Hydrogen Energy 44(19), 9486–9504 (2019)CrossRef
9.
Gelabert, L., Labandeira, X., Linares, P.: An ex-post analysis of the effect of renewables and cogeneration on Spanish electricity prices. Energy Econ. 33, S59–S65 (2011)CrossRef
10.
Woo, C.-K., Horowitz, I., Moore, J., Pacheco, A.: The impact of wind generation on the electricity spot-market price level and variance: the texas experience. Energy Policy 39(7), 3939–3944 (2011)CrossRef
11.
Würzburg, K., Labandeira, X., Linares, P.: Renewable generation and electricity prices: taking stock and new evidence for Germany and Austria. Energy Econ. 40, S159–S171 (2013)CrossRef
12.
Johanna C, Hauke H, Felix CM, Verena G (2014) Estimation and distributional implications: The merit order effect of wind and photovoltaic electricity generation in Germany 2008–2016. Energy Econ. 44, 302–313
13.
Benhmad, F., Percebois, J.: Photovoltaic and wind power feed-in impact on electricity prices: the case of Germany. Energy Policy 119, 317–326 (2018)CrossRef
14.
Janina, K.C.: The impact of wind power generation on the electricity price in Germany. Energy Econ. 44, 270–280 (2014)CrossRef
15.
Yang, S., Xuesong, X., Liu, J., Jiang, W.: Data-driven analysis of the real-time electricity price considering wind power effect. Energy Rep. 6, 452–459 (2020)CrossRef
16.
Gabriel de Freitas, V., Alves-Souza, S.N.: A systematic literature review on big data for solar photovoltaic electricity generation forecasting. Sustain. Energy Technol. Assess. 31, 54–63 (2019)
17.
Khosrojerdi, F., Akhigbe, O., Gagnon, S., Ramirez, A., Richards, G.: Integrating artificial intelligence and analytics in smart grids: a systematic literature review. Int. J. Energy Sector Manag. 16(2), 318–338 (2021)
18.
Keynia, F.: A new feature selection algorithm and composite neural network for electricity price forecasting. Eng. Appl. Artif. Intell. 25(8), 1687–1697 (2012)CrossRef
19.
Sansom, D.C., Downs, T., Saha, T.K.: Evaluation of support vector machine based forecasting tool in electricity price forecasting for Australian national electricity market participants. J. Elect. Electron. Eng. Aust. 22(3), 227 (2003)
20.
Mohammad M., Moin N., Shahram K.: Stock market prediction using twin Gaussian process regression. Int. J. Adv. Comput. Res. (JACR) preprint (2011)
21.
Guo, H., Chen, Q., Zheng, K., Xia, Q., Kang, C.: Forecast aggregated supply curves in power markets based on LSTM model. IEEE Trans. Power Syst. 36(6), 5767–5779 (2021)CrossRef
22.
Ruan, G., Zhong, H., Shan, B., Tan, X.: Constructing demand-side bidding curves based on a decoupled full-cycle process. IEEE Trans. Smart Grid 12(1), 502–511 (2020)CrossRef
23.
Saeed M., Mohammad R. H., Ali V., Farzad F.: A review of machine learning applications in electricity market studies. In: 2020 3rd International Colloquium on Intelligent Grid Metrology (SMAGRIMET), pages 1–8. IEEE (2020)
24.
Vandael, S., Claessens, B., Ernst, D., Holvoet, T., Deconinck, G.: Reinforcement learning of heuristic EV fleet charging in a day-ahead electricity market. IEEE Trans. Smart Grid 6(4), 1795–1805 (2015)CrossRef
25.
Xiaozhen, L., Xingyu, X., Liang, X., Canhuang, D., Mugen, P., H Vincent, P.: Reinforcement learning-based microgrid energy trading with a reduced power plant schedule. IEEE Internet Things J. 6(6), 10728–10737 (2019)CrossRef
26.
Guo, H., Chen, Q., Yuxuan, G., Shahidehpour, M., Xia, Q., Kang, Chongqing: A data-driven pattern extraction method for analyzing bidding behaviors in power markets. IEEE Trans. Smart Grid 11(4), 3509–3521 (2019)CrossRef
27.
Huan, Z., Junhua, Z., Jing, Q., Gaoqi, L., Fushuan, W., Yusheng, Xue, Zhao Yang, Dong: Data-driven risk preference analysis in day-ahead electricity market. IEEE Trans. Smart Grid 12(3), 2508–2517 (2020)
28.
Li, Y., Huang, J., Liu, Y., Zhao, T., Zhou, Y., Zhao, Yong, Yuen, Chau: Day-ahead risk averse market clearing considering demand response with data-driven load uncertainty representation: a singapore electricity market study. Energy 254, 123923 (2022)CrossRef
29.
Qiu, D., Dong, Z., Ruan, G., Zhong, H., Strbac, G., Kang, Chongqing: Strategic retail pricing and demand bidding of retailers in electricity market: a data-driven chance-constrained programming. Adv. Appl. Energy 7, 100100 (2022)CrossRef
30.
Mahmood, H.I., Payam, N., Barzegaran, M.R.: Implementing time-of-use demand response program in microgrid considering energy storage unit participation and different capacities of installed wind power. Electric Power Syst. Res. 175, 105916 (2019)CrossRef
31.
Ruan, G., Zhong, H., Wang, J., Xia, Q., Kang, C.: Neural-network-based Lagrange multiplier selection for distributed demand response in smart grid. Appl. Energy 264, 114636 (2020)CrossRef
32.
Pritchard, A.: Statistical bibliography or bibliometrics. J. Document. 25(4), 348–349 (1969)
33.
Lu H., Fangyan L., Yi Z.: Overlapping community discovery for identifying key research themes. IEEE Trans. Eng. Manag. (2020)
34.
Mohammad, F.M., Massimiliano, M.P., Giacomo, M., Marina, D.: Knowledge management in the fourth industrial revolution: mapping the literature and scoping future avenues. IEEE Trans. Eng. Manag. 68(1), 289–300 (2020)
35.
David, M., Alessandro, L., Jennifer, T., Douglas, G.A., Prisma, G.: Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med 6(7), e1000097 (2009)CrossRef
36.
Aria, M., Cuccurullo, C.: bibliometrix: an R-tool for comprehensive science mapping analysis. J. Inform. 11(4), 959–975 (2017)CrossRef
37.
Conejo, A.J., Plazas, M.A., Espinola, R., Molina, A.B.: Day-ahead electricity price forecasting using the wavelet transform and ARIMA models. IEEE Trans. Power Syst. 20(2), 1035–1042 (2005)CrossRef
38.
Sensfuß, F., Ragwitz, M., Genoese, M.: The merit-order effect: a detailed analysis of the price effect of renewable electricity generation on spot market prices in Germany. Energy Policy 36(8), 3086–3094 (2008)CrossRef
39.
Tao, H., Pierre, P., Shu, F., Hamidreza, Z., Alberto, T., Rob J. H.: Probabilistic energy forecasting: global energy forecasting competition 2014 and beyond (2016)
40.
da Silva Catalão, J.P., Mariano, S.J.P.S., Mendes, V.M.F., Ferreira, L.A.F.M.: Short-term electricity prices forecasting in a competitive market: a neural network approach. Electric Power Syst. Res. 77(10), 1297–1304 (2007)CrossRef
41.
Rodriguez, C.P., Anders, G.J.: Energy price forecasting in the Ontario competitive power system market. IEEE Trans. Power Syst. 19(1), 366–374 (2004)CrossRef
42.
Lago, J., De Ridder, F., De Schutter, B.: Forecasting spot electricity prices: deep learning approaches and empirical comparison of traditional algorithms. Appl. Energy 221, 386–405 (2018)CrossRef
43.
Tan, Z., Zhang, J., Wang, J., Jun, X.: Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models. Appl. energy 87(11), 3606–3610 (2010)CrossRef
44.
Paulo, J., da Silva, C., Hugo, M.I.P., Víctor, M.F.M.: Hybrid wavelet-PSO-ANFIS approach for short-term electricity prices forecasting. IEEE Trans. Power Syst. 26(1), 137–144 (2010)
45.
Yang, Z., Ce, L., Lian, L.: Electricity price forecasting by a hybrid model, combining wavelet transform, ARMA and kernel-based extreme learning machine methods. Appl. Energy 190, 291–305 (2017)CrossRef
46.
Panapakidis, I.P., Dagoumas, A.S.: Day-ahead electricity price forecasting via the application of artificial neural network based models. Appl. Energy 172, 132–151 (2016)CrossRef
47.
Clò, S., Cataldi, A., Zoppoli, P.: The merit-order effect in the Italian power market: the impact of solar and wind generation on national wholesale electricity prices. Energy Policy 77, 79–88 (2015)CrossRef
48.
Chen, X., Dong, Z.Y., Meng, K., Xu, Y., Wong, K.P., Ngan, H.W.: Electricity price forecasting with extreme learning machine and bootstrapping. IEEE Trans. Power Syst. 27(4), 2055–2062 (2012)CrossRef
49.
Wang, D., Luo, H., Grunder, O., Lin, Y., Guo, H.: Multi-step ahead electricity price forecasting using a hybrid model based on two-layer decomposition technique and BP neural network optimized by firefly algorithm. Appl. Energy 190, 390–407 (2017)CrossRef
50.
Abbas, R.G., Noradin, G.: A new feature selection and hybrid forecast engine for day-ahead price forecasting of electricity markets. J. Intell. Fuzzy Syst. 32(6), 4031–4045 (2017)CrossRef
51.
Li, G., Liu, C.-C., Mattson, C., Lawarrée, J.: Day-ahead electricity price forecasting in a grid environment. IEEE Trans. Power Syst. 22(1), 266–274 (2007)CrossRef
52.
Mandal, P., Senjyu, T., Urasaki, N., Funabashi, T., Srivastava, A.K.: A novel approach to forecast electricity price for PJM using neural network and similar days method. IEEE Trans. Power Syst. 22(4), 2058–2065 (2007)CrossRef
53.
Wan, C., Xu, Z., Wang, Y., Dong, Z.Y., Wong, K.P.: A hybrid approach for probabilistic forecasting of electricity price. IEEE Trans. Smart Grid 5(1), 463–470 (2013)CrossRef
54.
Keles, D., Scelle, J., Paraschiv, F., Fichtner, W.: Extended forecast methods for day-ahead electricity spot prices applying artificial neural networks. Appl. energy 162, 218–230 (2016)CrossRef
55.
Yamin, H.Y., Shahidehpour, S.M., Li, Z.: Adaptive short-term electricity price forecasting using artificial neural networks in the restructured power markets. Int. J. Electrical Power Energy Syst. 26(8), 571–581 (2004)CrossRef
56.
Zhao, J.H., Dong, Z.Y., Xu, Z., Wong, K.P.: A statistical approach for interval forecasting of the electricity price. IEEE Trans. Power Syst. 23(2), 267–276 (2008)CrossRef
57.
Peng, L., Liu, S., Liu, R., Wang, L.: Effective long short-term memory with differential evolution algorithm for electricity price prediction. Energy 162, 1301–1314 (2018)CrossRef
58.
Mandal, P., Senjyu, T., Funabashi, T.: Neural networks approach to forecast several hour ahead electricity prices and loads in deregulated market. Energy Conv. Manag 47(15–16), 2128–2142 (2006)CrossRef
59.
Singhal, D.: Electricity price forecasting using artificial neural networks. Int. J. Electrical Power Energy Syst. 33(3), 550–555 (2011)CrossRef
60.
Jun Hua, Z., Zhao Yang, D., Xue, L., Kit, P.W.: A framework for electricity price spike analysis with advanced data mining methods. IEEE Trans. Power Syst. 22(1), 376–385 (2007)CrossRef
61.
Akbilgic, O., Bozdogan, H., Balaban, M.E.: A novel hybrid RBF neural networks model as a forecaster. Stat. Comput. 24(3), 365–375 (2014)MathSciNetMATHCrossRef
62.
Anbazhagan, S., Kumarappan, N.: Day-ahead deregulated electricity market price forecasting using recurrent neural network. IEEE Syst. J. 7(4), 866–872 (2012)CrossRef
63.
64.
González, C., Mira-McWilliams, J., Juárez, I.: Important variable assessment and electricity price forecasting based on regression tree models: classification and regression trees, bagging and random forests. IET Gen. Transmiss. Distrib. 9(11), 1120–1128 (2015)CrossRef
65.
Sutton, C.D.: Classification and regression trees, bagging, and boosting. Handb. Stat. 24, 303–329 (2005)CrossRef
66.
Todd Farrell, M., Andrew, C.: Gaussian process regression models for predicting stock trends. Relation 10, 3414 (2007)
67.
Kang, J., Lee, S.: Data-driven prediction of load curtailment in incentive-based demand response system. Energies 11(11), 2905 (2018)CrossRef
68.
Hosseini Imani, M., Niknejad, P., Barzegaran, M.R.: The impact of customers’ participation level and various incentive values on implementing emergency demand response program in microgrid operation. Int. J. Elect. Power Energy Syst. 96, 114–125 (2018)CrossRef
69.
Hua, H., Qin, Y., Hao, C., Cao, J.: Optimal energy management strategies for energy Internet via deep reinforcement learning approach. Appl. Energy 239, 598–609 (2019)CrossRef
70.
Imani, M.H., Jabbari, Ghadi M., Ghavidel, S., Li, L.: Demand response modeling in microgrid operation: a review and application for incentive-based and time-based programs. Renew. Sustain. Energy Rev. 94, 486–499 (2018)CrossRef
71.
Renzhi, L., Hong, S.H., Zhang, X.: reinforcement learning approach: a dynamic pricing demand response algorithm for smart grid. Appl. Energy 220, 220–230 (2018)CrossRef
72.
Brida V.M., Mahtab K., Geert D.: Battery scheduling in a residential multi-carrier energy system using reinforcement learning. In: 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), pages 1–6. IEEE (2018)
73.
Wan, Z., Li, H., He, H., Prokhorov, D.: Model-free real-time EV charging scheduling based on deep reinforcement learning. IEEE Trans. Smart Grid 10(5), 5246–5257 (2018)CrossRef
74.
Mahmood, H. I., Ettore, B., Pietro, C., Tao H.: Impact of wind and solar generation on the Italian zonal electricity price (2021)
75.
Loumakis, S., Giannini, E., Maroulis, Z.: Merit order effect modeling: the case of the hellenic electricity market. Energies 12(20), 3869 (2019)CrossRef
76.
Quint, D., Dahlke, S.: The impact of wind generation on wholesale electricity market prices in the midcontinent independent system operator energy market: an empirical investigation. Energy 169, 456–466 (2019)CrossRef
77.
Csereklyei, Z., Songze, Q., Ancev, T.: The effect of wind and solar power generation on wholesale electricity prices in Australia. Energy Policy 131, 358–369 (2019)CrossRef
78.
Nuno, C.F., da Silva, P.P.: The “Merit-order effect’’ of wind and solar power: volatility and determinants. Renew. Sustain. Energy Rev. 102, 54–62 (2019)CrossRef
79.
Xingyu, X., Canhuang, D., Yanda, L., Changhua, Z., Liang, X.: Energy trading game for microgrids using reinforcement learning. In: International Conference on Game Theory for Networks, pages 131–140. Springer (2017)
80.
Ioannis, B., Damien, E., Bertrand C.: Real-time bidding strategies from micro-grids using reinforcement learning (2018)
81.
Wang, H., Huang, T., Liao, X., Abu-Rub, H., Chen, G.: Reinforcement learning for constrained energy trading games with incomplete information. IEEE Trans. Cybern. 47(10), 3404–3416 (2016)CrossRef
82.
Salehizadeh, M.R., Soltaniyan, S.: Application of fuzzy Q-learning for electricity market modeling by considering renewable power penetration. Renew. Sustain. Energy Rev. 56, 1172–1181 (2016)CrossRef
83.
Oliveira, P., Pinto, T., Morais, H., Vale, Z.A., Isabel P.: MASCEM-An electricity market simulator providing coalition support for virtual power players. In: 2009 15th International Conference on Intelligent System Applications to Power Systems, pages 1–6. IEEE (2009)
84.
Kumar, N., Tripathi, M. M.: Evaluation of effectiveness of ANN for feature selection based electricity price forecasting. In: 2017 International Conference on Emerging Trends in Computing and Communication Technologies (ICETCCT), pages 1–5. IEEE (2017)
85.
Anbazhagan, S., Kumarappan, N.: Day-ahead deregulated electricity market price forecasting using neural network input featured by DCT. Energy Convers. Manag. 78, 711–719 (2014)CrossRef
86.
de Simón-Martín, M., Bracco, S., Rosales-Asensio, E., Piazza, G., Delfino, F., Giuseppe Giribone, P.: Electricity Spot Prices Forecasting for MIBEL by using Deep Learning: a comparison between NAR, NARX and LSTM networks. In 2020 IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I &CPS Europe), pages 1–6. IEEE (2020)
87.
Zhang, J., Wang, F., Wang, R., Hou. A study on the application of BPNN based on minimum error entropy in electricity price forecasting. In 2011 6th IEEE Conference on Industrial Electronics and Applications, pages 925–929. IEEE (2011)
88.
Bhagat, M., Alamaniotis, M. Fevgas, A.: Extreme interval electricity price forecasting of wholesale markets integrating ELM and fuzzy inference. In 2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA), pages 1–4. IEEE (2019)
89.
Jessie Rani, R.H., Albert Victoire, T.A.: A hybrid Elman recurrent neural network, group search optimization, and refined VMD-based framework for multi-step ahead electricity price forecasting. Soft Comput. 23(18), 8413–8434 (2019)CrossRef
90.
Huang, C.-J., Shen, Y., Chen, Y.-H., Chen, H.-C.: A novel hybrid deep neural network model for short-term electricity price forecasting. Int. J. Energy Res. 45(2), 2511–2532 (2021)CrossRef
91.
Zou, Y., Tu, M., Teng, X., Cao, R., Xie, W.: Electricity price forecast based on stacked autoencoder in spot market environment. In 2019 9th International Conference on Power and Energy Systems (ICPES), pages 1–6. IEEE (2019)
92.
Ugurlu, U., Oksuz, I., Tas, O.: Electricity price forecasting using recurrent neural networks. Energies 11(5), 1255 (2018)CrossRef
93.
Ghayekhloo, M., Azimi, R., Ghofrani, M., Menhaj, M.B., Shekari, E.: A combination approach based on a novel data clustering method and Bayesian recurrent neural network for day-ahead price forecasting of electricity markets. Electric Power Syst. Res. 168, 184–199 (2019)CrossRef
94.
Zhou, S., Zhou, L., Mao, M., Tai, H.-M., Wan, Y.: An optimized heterogeneous structure LSTM network for electricity price forecasting. IEEE Access 7, 108161–108173 (2019)CrossRef
95.
Kuo, P.-H., Huang, C.-J.: An electricity price forecasting model by hybrid structured deep neural networks. Sustainability 10(4), 1280 (2018)CrossRef
96.
Hafeez, G., Saleem Alimgeer, K., Khan, I.: Electric load forecasting based on deep learning and optimized by heuristic algorithm in smart grid. Appl. Energy 269, 114915 (2020)CrossRef
97.
Ferreira, Â. P., Gonçalves Ramos, J., Odete Fernandes, P.: A linear regression pattern for electricity price forecasting in the Iberian electricity market. Revista Facultad de Ingeniería Universidad de Antioquia 93:117–127 (2019)
98.
Aggarwal, S. K., Mohan Saini, L., Kumar, A.: Electricity price forecasting in Ontario electricity market using wavelet transform in artificial neural network based model. Int. J. Control Autom. Syst. 6(5):639–650 (2008)
99.
Murat Sirin S., Yilmaz, B. N.: Variable renewable energy technologies in the Turkish electricity market: Quantile regression analysis of the merit-order effect. Energy Policy 144:111660 (2020)
100.
Qiu, X., Suganthan, P.N., Amaratunga, G.A.J.: Short-term electricity price forecasting with empirical mode decomposition based ensemble kernel machines. Procedia Comput. Sci. 108, 1308–1317 (2017)CrossRef
101.
Mirakyan, A., Meyer-Renschhausen, M., Koch, A.: Composite forecasting approach, application for next-day electricity price forecasting. Energy Econ. 66, 228–237 (2017)CrossRef
102.
O’Mahoney, A., Denny, E.: The merit order effect of wind generation on the Irish electricity market. In: Proceedings of the 30th USAEE/IAEEE North American Conference (2011)
103.
dos Santos Coelho, L., Santos, A.A.P.: A RBF neural network model with GARCH errors: application to electricity price forecasting. Electric Power Syst. Res. 81(1):74–83 (2011)
104.
Macedo, D. P., Marques, A. C., Damette, O.: The impact of the integration of renewable energy sources in the electricity price formation: is the Merit-Order Effect occurring in Portugal? Utilities Policy 66:101080 (2020)
105.
Bibi, N., Shah, I., Alsubie, A., Ali, S., Lone, S.A.: Electricity spot prices forecasting based on ensemble learning. IEEE Access 9, 150984–150992 (2021)CrossRef
106.
Zhao, Z., Wang, C., Nokleby, M., Miller, C.J.: Improving short-term electricity price forecasting using day-ahead LMP with ARIMA models. In: 2017 IEEE Power & Energy Society General Meeting, pages 1–5. IEEE (2017)
107.
McHugh, C., Coleman, S., Kerr, D., McGlynn, D.: A linear polynomial narmax model with multiple factors to forecast day-ahead electricity prices. In: 2018 IEEE Symposium Series on Computational Intelligence (SSCI), pages 2125–2130. IEEE (2018)
108.
Mestre, G., Portela, J., Roque, A.M.S., Alonso, E.: Forecasting hourly supply curves in the Italian Day-Ahead electricity market with a double-seasonal SARMAHX model. Int. J. Electrical Power Energy Syst. 121, 106083 (2020)CrossRef
109.
McHugh, C., Coleman, S., Kerr, D., McGlynn, D.: Forecasting day-ahead electricity prices with a SARIMAX model. In 2019 IEEE Symposium Series on Computational Intelligence (SSCI), pages 1523–1529. IEEE (2019)
110.
Papaioannou, G.P., Dikaiakos, C., Dramountanis, A., Papaioannou, P.G.: Analysis and modeling for short-to medium-term load forecasting using a hybrid manifold learning principal component model and comparison with classical statistical models (SARIMAX, Exponential Smoothing) and artificial intelligence models (ANN, SVM): The. Energies 9(8), 635 (2016)CrossRef
111.
Alamaniotis, M., Tsoukalas, L. H.: Anticipation of minutes-ahead household active power consumption using Gaussian processes. In: 2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA), pages 1–6. IEEE (2015)
112.
Hosseini Imani, M., Bompard, E., Colella, P., Huang, T.: Forecasting electricity price in different time horizons: an application to the Italian electricity market. IEEE Trans. Ind. Appl. (2021)
113.
Brahmia, I., Wang, J., Haotian, X., Wang, H., De Oliveira, L.T.: Robust data predictive control framework for smart multi-microgrid energy dispatch considering electricity market uncertainty. IEEE Access 9, 32390–32404 (2021)CrossRef
114.
Sara, A., Amr, B.E.: A comparative study using deep learning and support vector regression for electricity price forecasting in smart grids. In: 2019 IEEE 6th International Conference on Industrial Engineering and Applications (ICIEA), pages 603–607. IEEE (2019)
115.
Wei, S., Jie, Z.: Forecasting day ahead spot electricity prices based on GASVM. In: 2008 International Conference on Internet Computing in Science and Engineering, pages 73–78. IEEE (2008)
116.
Ghasemi, A., Shayeghi, H., Moradzadeh, M., Nooshyar, M.: A novel hybrid algorithm for electricity price and load forecasting in smart grids with demand-side management. Appl. Energy 177, 40–59 (2016)CrossRef
117.
Mari, C., Baldassari, C.: Ensemble methods for Jump–Diffusion models of power prices. Energies 14(8), 2084 (2021)CrossRef
118.
Bijay, N., Wei, L.W., Zeyar, A.: Ensemble prediction model with expert selection for electricity price forecasting. Energies 10(1), 77 (2017)CrossRef
119.
Lennard, V., Tarek, A., Wilfried, V.S.: The importance of predictor variables and feature selection in day-ahead electricity price forecasting. In: 2020 International Conference on Smart Energy Systems and Technologies (SEST), pages 1–6. IEEE (2020)
120.
Álvaro, R., José, R.D., Julia, D.: Day-ahead price forecasting for the spanish electricity market. Int J Interact Multimed Artif Intell (2018)
121.
Behrouz, B., Srimantoorao, S.A., Yuvraj, G., Aerambamoorthy, T.: Intelligent probabilistic forecasts of day-ahead electricity prices in a highly volatile power market. In: 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC), pages 166–175. IEEE (2021)
122.
Alexander, R.K., Ken’ichi, M., Kenta, T., Yogi, S., Shunsuke, M.: (2020) The impact of renewable energy generation on the spot market price in Germany: ex-post analysis using boosting method. Energy J 41(Special Issue)
123.
Wang, F., Li, K., Zhou, L., Ren, H., Contreras, J., Shafie-Khah, M., Catalão, J.P.S.: Daily pattern prediction based classification modeling approach for day-ahead electricity price forecasting. Int J Elect Power Energy Syst 105, 529–540 (2019)CrossRef
124.
Francisco, M.-Á., Troncoso, A., Riquelme, J.C., Riquelme, J.M.: Partitioning-clustering techniques applied to the electricity price time series. In: International Conference on Intelligent Data Engineering and Automated Learning, pages 990–999. Springer (2007)
125.
Alicia, T.L., Jesús, R.S., José, R.S., Antonio, G.E., José, L.M.R.: A comparison of two techniques for next-day electricity price forecasting. In: International Conference on Intelligent Data Engineering and Automated Learning, pages 384–390. Springer (2002)
126.
Hiroyuki, M., Akira, A.: Normalized RBFN with hierarchical deterministic annealing clustering for electricity price forecasting. In: 2007 IEEE Power Engineering Society General Meeting, pages 1–7. IEEE (2007)
127.
Fátima, R., Vera, F., Zita, V.: An integrated system to support electricity tariff contract definition. In: Frontiers in Artificial Intelligence and Applications, volume 218, pages 99–109. IOS Press (2010)
128.
Niu, D., Liu, D., Dash Wu, D.: A soft computing system for day-ahead electricity price forecasting. Appl. Soft Comput. 10(3), 868–875 (2010)CrossRef
129.
Amjady, N., Keynia, F.: Electricity market price spike analysis by a hybrid data model and feature selection technique. Electric Power Syst. Res. 80(3), 318–327 (2010)CrossRef
130.
Vipin, K., Nitin, S., Deepak, K.S., Mohanty, S.R.: Short-term electricity price forecasting using hybrid SARIMA and GJR-GARCH model. In: Networking communication and data knowledge engineering, pages 299–310. Springer (2018)
131.
Zhang, F., Fleyeh, H., Bales, C.: A hybrid model based on bidirectional long short-term memory neural network and Catboost for short-term electricity spot price forecasting. J. Oper. Res. Soc. 73(2), 301–325 (2022)CrossRef
132.
Weron, R.: Electricity price forecasting: a review of the state-of-the-art with a look into the future. Int. J. Forecast 30(4), 1030–1081 (2014)CrossRef
133.
Lago, J., De Ridder, F., Vrancx, P., De Schutter, B.: Forecasting day-ahead electricity prices in Europe: the importance of considering market integration. Appl. Energy 211, 890–903 (2018)CrossRef
134.
Yang, W., Wang, J., Niu, T., Pei, D.: A hybrid forecasting system based on a dual decomposition strategy and multi-objective optimization for electricity price forecasting. Appl. Energy 235, 1205–1225 (2019)CrossRef
135.
Qiao, W., Yang, Z.: Forecast the electricity price of US using a wavelet transform-based hybrid model. Energy 193, 116704 (2020)CrossRef
136.
Bento, P.M.R., Pombo, J.A.N., Calado, M.R.A., Mariano, S.J.P.S.: A bat optimized neural network and wavelet transform approach for short-term price forecasting. Appl. Energy 210, 88–97 (2018)CrossRef
137.
Ziel, F., Weron, R.: Day-ahead electricity price forecasting with high-dimensional structures: univariate vs. multivariate modeling frameworks. Energy Econ. 70, 396–420 (2018)CrossRef
138.
Chang, Z., Zhang, Y., Chen, W.: Electricity price prediction based on hybrid model of adam optimized LSTM neural network and wavelet transform. Energy 187, 115804 (2019)CrossRef
139.
Maheen, Z., Fahad, A., Ja, Nadeem, Raza Abid, A., Hafiza, S., Zainab, K., Atia, J., Muhammad, B., Mariam, A., Manzoor, I.: Electricity price and load forecasting using enhanced convolutional neural network and enhanced support vector regression in smart grids. Electronics 8(2), 122 (2019)CrossRef
140.
Zhang, X., Wang, J., Gao, Y.: A hybrid short-term electricity price forecasting framework: Cuckoo search-based feature selection with singular spectrum analysis and SVM. Energy Econ. 81, 899–913 (2019)CrossRef
141.
Deng, Z., Liu, C., Zhu, Z.: Inter-hours rolling scheduling of behind-the-meter storage operating systems using electricity price forecasting based on deep convolutional neural network. Int. J. Elect. Power Energy Syst. 125, 106499 (2021)CrossRef
142.
Heydari, A., Nezhad, M.M., Pirshayan, E., Garcia, D.A., Keynia, F., De Santoli, L.: Short-term electricity price and load forecasting in isolated power grids based on composite neural network and gravitational search optimization algorithm. Appl. Energy 277, 115503 (2020)CrossRef
143.
Zhang, J., Tan, Z., Wei, Y.: An adaptive hybrid model for short term electricity price forecasting. Appl. Energy 258, 114087 (2020)CrossRef
144.
Jahangir, H., Tayarani, H., Baghali, S., Ahmadian, A., Elkamel, A., Golkar, Masoud Aliakbar, Castilla, Miguel: A novel electricity price forecasting approach based on dimension reduction strategy and rough artificial neural networks. IEEE Trans. Ind. Inform. 16(4), 2369–2381 (2019)CrossRef
145.
Yang, W., Wang, J., Niu, T., Pei, D.: A novel system for multi-step electricity price forecasting for electricity market management. Appl. Soft Comput. 88, 106029 (2020)CrossRef
146.
Zhang, S., Chen, Y., Zhang, W., Feng, R.: A novel ensemble deep learning model with dynamic error correction and multi-objective ensemble pruning for time series forecasting. Inform. Sci. 544, 427–445 (2021)MathSciNetCrossRef
147.
Zhang, H., Yi Yang, Y., Zhang, Z.H., Yuan, W., Yang, Y., Qiu, W., Li, L.: A combined model based on SSA, neural networks, and LSSVM for short-term electric load and price forecasting. Neural Comput. Appl. 33(2), 773–788 (2021)CrossRef
148.
Venkateswarlu, G., Simon, S.P.: PSO-LSTM for short term forecast of heterogeneous time series electricity price signals. J. Ambient Intell. Hum. Comput. 12(2), 2375–2385 (2021)CrossRef
Metadata
Title
Data analytics in the electricity market: a systematic literature review
Authors
Mahmood Hosseini Imani
Ettore Bompard
Pietro Colella
Tao Huang
Publication date
08-04-2023
Publisher
Springer Berlin Heidelberg
Published in
Energy Systems
Print ISSN: 1868-3967
Electronic ISSN: 1868-3975
DOI
https://doi.org/10.1007/s12667-023-00576-1