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Tytuł artykułu

Power Flow Forecasts: A Status Quo Review. Part 2: Electricity Demand and Power Flow Prediction

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Warianty tytułu
PL
Prognozy przepływów mocy-przegląd status quo. Część 2: Predykcja zapotrzebowania na energię i przepływów mocy
Języki publikacji
EN
Abstrakty
EN
Electricity demand predictions are one of the most important tools used for Power System work planning. However, a departure from traditional solutions seems to be inevitable in the light of ever-increasing RES share. This paper is the second of a two-part extensive review of recent literature related to forecasts of RES generation, electricity demand and power flows, and presents the second and third of the mentioned aspects.
PL
Prognozy zapotrzebowania na energię są jednym z najważniejszych narzędzi w Planowaniu pracy SEE. Odejście od ich klasycznych rozwiązań wydaje się być jednak nieuniknione w świetle coraz bardziej zwieszającej się liczby OZE. Niniejszy artykuł to druga z 2 części szerokiej analizy najnowszej literatury dotyczącej prognoz generacji z OZE, zapotrzebowania i przepływów mocy. Prezentuje on 2 i 3 aspekt.
Słowa kluczowe
Rocznik
Strony
5--10
Opis fizyczny
Bibliogr.32 poz., tab.
Twórcy
autor
  • Politechnika Warszawska, Instytut Elektroenergetyki, ul. Koszykowa 75, 00-662 Warszawa
Bibliografia
  • [1] Dudek G, Short-term load forecasting using Theta method, 14th International Scientific Conference “Forecasting in Electric Power Engineering” (PE 2018)E3S Web of Conferences 84, 01004 (2019), https://doi.org/10.1051/e3sconf /20198401004
  • [2] Dudek G., Pełka P., Prognozowanie miesięcznego zapotrzebowania na energię elektryczną metodą k najbliższych sąsiadów, Przeglad Elektrotechniczny 1(4), (2017), 64-67
  • [3] Popławski T., Dudek G., Łyp J., Forecasting methods for balancing energy market in Poland, Electrical Power and Energy Systems, 65 (2015) 94–101
  • [4] Gong G., An X, Mahato N.K , Sun S., Chen S.,Wen Y., Research on Short-Term Load Prediction Based on Seq2seq Model, Energies, 2019, 12, 3199
  • [5] Rodrigues F., Trindade A., Load forecasting through functional clustering and ensemble learning, Knowl Inf Syst, 57 (2018), 229–244
  • [6] Tian C., Ma J., Zhang C., Zhan P., A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short- Term Memory Network and Convolutional Neural Network, Energies,11 (2018), 3493
  • [7] Ghadimi N., Akbarimajd A., Shayeghi H., Abedinia O., Two stage forecast engine with feature selection technique and improved meta-heuristic algorithm for electricity load forecasting, Energy, 161 (2018), 130-142
  • [8] Li W., Yang X., Li H. ,Su L., Hybrid Forecasting Approach based on GRNN Neural Network and SVR Machine for Electricity Demand Forecasting, Energies, 2017, 10, 44;
  • [9] Xiao L.,Shao W.,Yu M., Ma J., Jin C., Research and application of a hybrid wavelet neural network model with the improved cuckoo search algorithm for electrical power system forecasting, Applied Energy, 198(2017),203-222
  • [10] Hu R., Wen S., Zeng Z., Huang T., A short-term power load forecasting model based on the generalized regression neural network with decreasing step fruit fly optimization algorithm, Neurocomputing, 221 (2017), 24–31
  • [11] Tarsitano A., Amerise I.L., Short-term load forecasting using a two-stage sarimax model, Energy, 133 (2017), 108-114
  • [12] Sowiński J., Forecasting of electricity demand in the region, E3S Web of Conferences 84, 01010 (2019), https://doi.org/10.1051/e3sconf /20198401010
  • [13] Abreu T., Amorim A. J., Santos-Junior C.R., Lotufo A.D.P., Minussi C.R., Multinodal load forecasting for distribution systems using afuzzy-artmap neural network, Applied Soft Computing, 71 (2018), 307–316
  • [14] Amorim A.J., Abreu T.A., Tonelli-Neto M.S.,Minussi C.R., A new formulation of multinodal short-term load forecasting based on adaptive resonance theory with reverse training, Electric Power Systems Research, 179 (2020), 106096
  • [15] Xu L., Wang S., Tang R., Probabilistic load forecasting for buildings considering weather forecasting uncertainty and uncertain peak load, Applied Energy, 237 (2019), 180–195
  • [16] Mohammadi M., Talebpour F., Safaee E., Ghadimi N. Abedinia O., Small-Scale Building Load Forecast based on Hybrid Forecast Engine, Neural Process Lett,48 (2018),329– 351
  • [17] Zheng Z., Chen H., Luo X., A Kalman filter-based bottom-up approach for household short-term load forecast, Applied Energy, 250 (2019), 882–894
  • [18] Chen Y.,Xu P., Chu Y., Li W., Wu Y., Ni L., Bao Y., Wang K., Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings, Applied Energy, 195 (2017), 659–670
  • [19] Dai Sh., Niu D., Li Y., Daily Peak Load Forecasting Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Support Vector Machine Optimized by Modified GreyWolf Optimization Algorithm, Energies, 2018, 11, 163
  • [20] Elamin N., Fukushige M., Quantile Regression Model for Peak Load Demand Forecasting with Approximation by Triangular Distribution to Avoid Blackouts, International Journal of Energy Economics and Policy, 2018, 8(5), 119- 124
  • [21] Zhao H., Han X., Guo S., DGM (1, 1) model optimized by MVO (multi-verse optimizer) for annual peak load forecasting, Neural Comput & Applic, (2018) 30:1811–1825
  • [22] Miranda S.T., Abaide A., Sperandio M., Santos M.M., Zanghi E., Application of artificial neural networks and fuzzy logic to long-term load forecast considering the price elasticity of electricity demand, Int Trans Electr Energ Syst., 2018;28:e2606.,
  • [23] Ali D., Yohanna M., Ijasini P.M., Garkida M.B., Application of fuzzy – Neuro to model weather parameter variability impacts on electrical load based on long-term forecasting, Alexandria Engineering Journal,57 (2018), 223–233
  • [24] Rajanarayan Prusty B., Debashisha Jena, A spatiotemporal probabilistic model‐based temperature‐augmented probabilistic load flow considering PV generations, International Transactions on Electrical Energy Systems, 29 (2019), no. 5
  • [25] Fang X ,Hodge B-M. ,Du E., Zhang N., Li F., Modelling wind power spatial-temporal correlation in multi-interval optimal power flow: A sparse correlation matrix approach, Applied Energy, 230 (2018), 531-539
  • [26] Kathiravan R., Devi R. P. K., Optimal power flow model incorporating wind, solar, and bundled solar-thermal power in the restructured Indian power system, INTERNATIONAL JOURNAL OF GREEN ENERGY,14 ( 2017) , NO. 11, 934–950
  • [27] Massidda L., Marrocu M., Decoupling Weather Influence from User Habits for an Optimal Electric Load Forecast System, Energies, 2017, 10, 2171
  • [28] Haupt S.E, Dettling S., Williams J. K., Pearson J., Jensen T., Brummet T., Kosovic B., Wiener G., McCandles T., Burghardt C., Blending distributed photovoltaic and demand load forecasts, Solar Energy, 157 (2017), 542–551
  • [29] Kaur A., Nonnenmacher L., Coimbra C.F.M., Net load forecasting for high renewable energy penetration grids, Energy, 114 (2016), 1073-1084
  • [30] Sepasi S., Reihani E., Howlader A.M., Roose L.R, Matsuura M. M., Very short term load forecasting of a distribution system with high PV penetration, Renewable Energy, 106 (2017), 142-148
  • [31] Wang Y., Zhang N., Chen Q., Kirschen D.S., Li P., Xia Q., Data-Driven Probabilistic Net Load Forecasting With High Penetration of Behind-the-Meter PV, IEEE TRANSACTIONS ON POWER SYSTEMS, 33(2018), NO. 3
  • [32] Li Y., Wen Z., Cao Y., Tan Y., Sidorov D., Panasetsky D., A combined forecasting approach with model self-adjustment for renewable generations and enenergy loads in smart community, Energy, 129 (2017), 216-227
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-4ae17eb4-4333-4d6d-a1b0-a0e2d75bab89
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