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Статті в журналах з теми "Electricity price prediction"
Castelli, Mauro, Aleš Groznik, and Aleš Popovič. "Forecasting Electricity Prices: A Machine Learning Approach." Algorithms 13, no. 5 (May 8, 2020): 119. http://dx.doi.org/10.3390/a13050119.
Повний текст джерелаCao, Man, Yajun Wang, Jinning Liu, Zhiyong Yin, Xin Guo, and Xiaokun Ren. "Day Ahead Electricity Price Forecasting Based on the Deep Belief Network." Wireless Communications and Mobile Computing 2022 (September 29, 2022): 1–8. http://dx.doi.org/10.1155/2022/3960597.
Повний текст джерелаXie, Xiaoming, Meiping Li, and Du Zhang. "A Multiscale Electricity Price Forecasting Model Based on Tensor Fusion and Deep Learning." Energies 14, no. 21 (November 4, 2021): 7333. http://dx.doi.org/10.3390/en14217333.
Повний текст джерелаArvanitidis, Athanasios Ioannis, Dimitrios Bargiotas, Dimitrios Kontogiannis, Athanasios Fevgas, and Miltiadis Alamaniotis. "Optimized Data-Driven Models for Short-Term Electricity Price Forecasting Based on Signal Decomposition and Clustering Techniques." Energies 15, no. 21 (October 25, 2022): 7929. http://dx.doi.org/10.3390/en15217929.
Повний текст джерелаXie, Ke, Yiwang Luo, Wenjing Li, Zhipeng Chen, Nan Zhang, and Cai Liu. "Deep Learning with Multisource Data Fusion in Electricity Internet of Things for Electricity Price Forecast." Wireless Communications and Mobile Computing 2022 (January 24, 2022): 1–11. http://dx.doi.org/10.1155/2022/3622559.
Повний текст джерелаAsemota, Godwin Norense Osarumwense. "A Prediction Model of Future Electricity Pricing in Namibia." Advanced Materials Research 824 (September 2013): 93–99. http://dx.doi.org/10.4028/www.scientific.net/amr.824.93.
Повний текст джерелаWan Abdul Razak, Intan Azmira, Izham Zainal Abidin, Yap Keem Siah, and Mohamad Fani Sulaima. "NEXT-HOUR ELECTRICITY PRICE FORECASTING USING LEAST SQUARES SUPPORT VECTOR MACHINE AND GENETIC ALGORITHM." ASEAN Engineering Journal 12, no. 3 (August 31, 2022): 11–17. http://dx.doi.org/10.11113/aej.v12.17276.
Повний текст джерелаOksuz, Ilkay, and Umut Ugurlu. "Neural Network Based Model Comparison for Intraday Electricity Price Forecasting." Energies 12, no. 23 (November 29, 2019): 4557. http://dx.doi.org/10.3390/en12234557.
Повний текст джерелаZhang, Yangrui, Peng Tao, Xiangming Wu, Chenguang Yang, Guang Han, Hui Zhou, and Yinlong Hu. "Hourly Electricity Price Prediction for Electricity Market with High Proportion of Wind and Solar Power." Energies 15, no. 4 (February 13, 2022): 1345. http://dx.doi.org/10.3390/en15041345.
Повний текст джерелаLu, Ning, and Ying Liu. "A Research into Probabilistic Electricity Load Prediction Based on Demand Response Feature under Smart Grid Environment." Applied Mechanics and Materials 380-384 (August 2013): 3098–102. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.3098.
Повний текст джерелаДисертації з теми "Electricity price prediction"
Li, Jiasen. "Prediction of Electricity Price Quotation Data of Prioritized Clean Energy Power Generation of Power Plants in The Buyer's Market." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1627663082026476.
Повний текст джерелаChen, Jie. "Theoretical Results and Applications Related to Dimension Reduction." Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/19841.
Повний текст джерелаRuthberg, Richard, and Sebastian Wogenius. "Stochastic Modeling of Electricity Prices and the Impact on Balancing Power Investments." Thesis, KTH, Industriell ekonomi och organisation (Inst.), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-192111.
Повний текст джерелаI takt med att fler intermittenta förnyelsebara energikällor tillför el i dagens energisystem, blir också balanskraftens roll i dessa system allt viktigare. Vidare så har en ökning av andelen intermittenta förnyelsebara energikällor även effekten att de bidrar till lägre men också mer volatila elpriser. Därmed är även investeringar i balanskraft kopplade till stora risker med avseende på förväntade vinster, vilket gör att en god representation av elpriser är central vid investeringsbeslut. Vi föreslår en stokastisk flerfaktormodell för att simulera den långsiktiga dynamiken i elpriser som bas för värdering av generatortillgångar. Mer specifikt används modellen till att utvärdera effekten av elprisers dynamik på investeringsbeslut med avseende på balanskraft, där ett kraftvärmeverk studeras i detalj. Eftersom huvudmålet med ramverket är att skapa en långsiktig representation av elpriser så att deras fördelningsmässiga karakteristika bevaras, vilket i litteraturen citeras som regression mot medelvärde, säsongsvariationer, hög volatilitet och spikar, så utvärderas modellen i termer av årlig prisvaraktighet som beskriver fördelningen av elpriser över tid. Kärnan i ramverket utgår från Pilipovic-modellen av råvarupriser, men där vi utvecklar antaganden i ett flerfaktorramverk genom att lägga till en länkfunktion till tillgång- och efterfrågan på el samt utomhustemperatur. Vid användande av modellen som ett sätt att representera framtida priser, fås en maximal över- och underprediktion av prisvaraktighet om 9 procent, ett resultat som är bättre än det som ges av enklare modellering såsom säsongsprofiler eller enkla medelvärdesestimat som inte tar hänsyn till elprisernas fulla karakteristika. Till sist visar vi med modellens olika komponenter att variationer i elpriser, och därmed antaganden som används i långsiktig modellering, har stor betydelse med avseende på investeringsbeslut i balanskraft. Det realiserade värdet av flexibiliteten att producera el för ett kraftvärmeverk beräknas, vilket ger en värdering nära faktiska realiserade värden baserade på historiska priser och som enklare modeller inte kan konkurrera med. Slutligen visar detta också att inkluderandet av icke-konstant volatilitet och spikkarakteristika i investeringsbeslut ger ett högre förväntat värde av tillgångar som kan producera balanskraft, såsom kraftvärmeverk.
André, Léo. "Prediction of French day-ahead electricity prices: Comparison between a deterministic and a stochastic approach." Thesis, KTH, Matematisk statistik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-163721.
Повний текст джерелаDenna avhandling behandlar den nya flödesbaserade beräkningsmetoden som används i Centrala Västeuropa på ekonomisidan. Målet är att producera tillförlitliga metoder för prognostisering. Två tillvägagångssätt kan användas: den första är baserad på en deterministisk och algoritmisk metod som inbegriper studier av interaktionen mellan fundamenta och priserna. Den andra är en mer statistisk metod som bygger på en tidsseriemodellering av de franska flödesbaserade priserna. Båda tillvägagångssätten har fördelar och nackdelar som kommer som diskuteras i det följande. Arbetet är främst baserade på globala simulerade data från CASC i genomförandefasen av flödesbasen i Västeuropa.
Nan, Fany. "Forecasting next-day electricity prices: from different models to combination." Doctoral thesis, Università degli studi di Padova, 2009. http://hdl.handle.net/11577/3426510.
Повний текст джерелаCon la liberalizzazione dei mercati dell’elettricità, il problema della modellazione e previsione dei prezzi elettrici è diventato di fondamentale importanza. In letteratura sono stati studiati e applicati ad un gran numero di mercati molti tipi di modelli, come modelli per serie storiche, regressione lineare e modelli non lineari a salti molto più complessi. I risultati però sono contrastanti e finora nessun modello ha mostrato una capacità previsiva dei prezzi elettrici superiore rispetto agli altri. L’obiettivo di questa tesi è capire se i modelli di combinazione di previsioni possano dare risultati statisticamente superiori rispetto alle previsioni ottenute da singoli modelli. In particolare, viene affrontato il problema della previsione dei prezzi elettrici del giorno dopo applicato al mercato elettrico britannico UK Power Exchange. In questo mercato, i prezzi hanno frequenza semioraria: al fine di valutare il comportamento previsivo dei modelli, relativamente all’andamento dei prezzi nei diversi momenti della giornata, sono state scelte specifiche fasce orarie. I modelli usati per la previsione dei prezzi sono stati stimati sulla base di finestre di dati espandibili e/o mobili di diverse misure fissate. I modelli considerati includono modelli lineari di tipo ARMAX e diverse specificazioni di modelli di regressione multipla. Inotre sono stati considerati modelli di regressione non lineare a regimi Markov switching e modelli di regressione a parametri non costanti. Le previsioni a un passo ottenute dai modelli specificati sono state confrontate secondo diversi criteri statistici come le statistiche basate sull’errore di previsione e il test di Diebold e Mariano. Dai risultati emerge che, globalmente, nessun modello considerato supera gli altri per abilità previsiva: vari fattori, tra cui specificazione del modello, realizzazione campionaria e periodo di previsione, influenzano l’accuratezza previsiva. Dal momento che modelli di previsione diversi sembrano evidenziare caratteristiche diverse della dinamica dei prezzi elettrici, viene proposto un approccio basato sulla combinazione di previsioni. Questo metodo, finalizzato a migliorare l’accuratezza previsiva, si è dimostrato utile in molti studi empirici, ma finora non è stato usato nel contesto della previsione dei prezzi elettrici. In questa tesi sono state usate diverse tecniche di combinazione. L’approccio più semplice consiste nel dare lo stesso peso a tutte le previsioni ottenute dai singoli modelli. Altre procedure di combinazione di previsioni sono di tipo adattivo, poichè utilizzano coefficienti non costanti. In questo contesto, sono stati considerati i metodi di Bates & Granger (1969). I modelli usati nella combinazione sono stati scelti, per ciascuna stagione di previsione, con il metodo model confidence set (MCS) descritto in Hansen et al. (2003, 2005) e successivamente ridotti con il metodo forecasts encompassing di Fair & Shiller (1990). Per ciascuna ora considerata, i risultati sottolineano che i modelli si comportano in modo diverso a seconda della stagione di previsione. Questa caratteristica giustifica l’applicazione dei modelli di combinazione di previsioni ad un livello stagionale. In questa tesi vengono presentati risultati promettenti in questa direzione. Considerando le statistiche basate sull’errore di previsione, i risultati delle combinazioni sono stati confrontati con i migliori risultati ottenuti dai singoli modelli in ciascun periodo previsivo. Il vantaggio della procedura proposta deriva dal fatto che combinando le previsioni ad un livello stagionale, si ottengono previsioni di accuratezza superiore o uguale rispetto alle previsioni individuali.
Januário, João Filipe Ferreira. "Electricity price forecasting utilizing machine learning in MIBEL." Master's thesis, 2019. http://hdl.handle.net/10071/20235.
Повний текст джерелаAlves, Ana Maria da Rocha de Sousa Guedes. "Is the Iberian electricity market chaotic? Characterization and prediction with nonlinear methods." Doctoral thesis, 2013. http://hdl.handle.net/10071/6927.
Повний текст джерелаCom a alteração do paradigma relativo aos sistemas eléctricos, deixando de ser regulados e passando a ser liberalizados, o estudo e a previsão de preços e de potências de carga nos sistemas eléctricos tornaram-se num novo tema de interesse para os investi- gadores. Devido às particularidades da electricidade, um mercado de electricidade tem regras muito especí cas que têm que ser compreendidas antes de se iniciar o seu estudo. Este trabalho apresenta um estudo sobre o mercado Ibérico de Electricidade, repres- entado pelas séries de potências de carga e de preços, segundo uma abordagem de sistemas dinâmicos deterministícos caóticos. O objectivo do trabalho consistiu em veri car se as séries de potências de carga e de preços apresentam características caóticas, reconstruindo os seus atractores e estimando alguns invariantes do sistema, tais como a dimensão de correlação, a entropia de Kolmogorov-Sinai e os expoentes de Lyapunov. A previsão para as próximas 24 horas pode então ser feita usando o método determinístico de coordenadas com atraso do tempo e redes neuronais arti ciais. Como resultado deste trabalho, foram identi cadas evidências de que tanto a série das potências de carga como a série dos preços de electricidade são regidas por um sistema dinâmico caótico e as suas previsões foram conseguidas com bastante sucesso.
With the paradigm shift regarding power systems, that used to be regulated and started to be liberalized, the study and forecast of prices and electricity demand have become a new topic of interest to researchers. Due to the peculiarities of electricity, electricity markets have very speci c rules that must be understood before starting their study. This thesis presents a study of the Iberian Electricity Market, represented by the series of demand and prices, in the framework of nonlinear deterministic chaos. The goal of this research was to verify that the series of demand and prices have chaotic features, reconstructing their attractors and estimating some invariants of the system as the correlation dimension, the Kolmogorov-Sinai entropy and the Lyapunov exponents. The forecast for the next 24 hours can then be done using deterministic tools like the method of time delay and arti cial neural networks. As a result of this research, we identi ed evidence that both the series of the demand and the series of electricity prices are governed by a chaotic dynamic system and their predictions were successfully achieved.
Частини книг з теми "Electricity price prediction"
Pal, Kirti, Laxmi Srivastava, and Manjaree Pandit. "Levenberg-Marquardt Algorithm Based ANN for Nodal Price Prediction in Restructured Power System." In Electricity Distribution, 297–318. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-49434-9_13.
Повний текст джерелаPao, Hsiao-Tien. "A Neural Network Approach to m-Daily-Ahead Electricity Price Prediction." In Advances in Neural Networks - ISNN 2006, 1284–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11760023_186.
Повний текст джерелаVardakas, John S., and Ioannis Zenginis. "A Survey on Short-Term Electricity Price Prediction Models for Smart Grid Applications." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 60–69. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18802-7_9.
Повний текст джерелаShuja, Sahibzada Muhammad, Nadeem Javaid, Sajjad Khan, Umair Sarfraz, Syed Hamza Ali, Muhammad Taha, and Tahir Mehmood. "Electricity Price Prediction by Enhanced Combination of Autoregression Moving Average and Kernal Extreme Learing Machine." In Advances in Intelligent Systems and Computing, 1145–56. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15035-8_110.
Повний текст джерелаXu, Feihong, Xianliang Teng, Jixiang Lu, Tao Zheng, and Yulong Jin. "Prediction of Day-Ahead Electricity Price Based on N-BEATSx Model Optimized by SSA Considering Coupling Between Features." In Proceedings of the 7th PURPLE MOUNTAIN FORUM on Smart Grid Protection and Control (PMF2022), 178–94. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0063-3_13.
Повний текст джерелаSinha, Ayush, Tinku Singh, Ranjana Vyas, Manish Kumar, and O. P. Vyas. "A Methodological Review of Time Series Forecasting with Deep Learning Model: A Case Study on Electricity Load and Price Prediction." In Lecture Notes in Electrical Engineering, 457–79. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-5868-7_34.
Повний текст джерелаScholz, Christoph, Malte Lehna, Katharina Brauns, and André Baier. "Towards the Prediction of Electricity Prices at the Intraday Market Using Shallow and Deep-Learning Methods." In Mining Data for Financial Applications, 101–18. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66981-2_9.
Повний текст джерелаHuang, Rui, and Lorenz T. Biegler. "Economic NMPC for energy intensive applications with electricity price prediction." In Computer Aided Chemical Engineering, 1612–16. Elsevier, 2012. http://dx.doi.org/10.1016/b978-0-444-59506-5.50153-x.
Повний текст джерелаSanchez, Edgar N., Alma Y. Alanis, and Jesús Rico. "Electric Load Demand and Electricity Prices ForecastingUsing Higher Order Neural Networks Trained by Kalman Filtering." In Artificial Higher Order Neural Networks for Economics and Business, 295–313. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-59904-897-0.ch013.
Повний текст джерелаFainti, Rafik, Miltiadis Alamaniotis, and Lefteri H. Tsoukalas. "Backpropagation Neural Network for Interval Prediction of Three-Phase Ampacity Level in Power Systems." In Deep Learning and Neural Networks, 883–904. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0414-7.ch049.
Повний текст джерелаТези доповідей конференцій з теми "Electricity price prediction"
Yan, Junchi, Chunhua Tian, Yu Wang, and Jin Huang. "Online incremental regression for electricity price prediction." In 2012 IEEE International Conference on Service Operations and Logistics and Informatics (SOLI). IEEE, 2012. http://dx.doi.org/10.1109/soli.2012.6273500.
Повний текст джерелаDeng, Hui, Fei Yan, Hao Wang, Le Fang, Ziqing Zhou, Feng Zhang, Chengwei Xu, and Haiyi Jiang. "Electricity Price Prediction Based on LSTM and LightGBM." In 2021 IEEE 4th International Conference on Electronics and Communication Engineering (ICECE). IEEE, 2021. http://dx.doi.org/10.1109/icece54449.2021.9674719.
Повний текст джерелаHuixin Tian, Bo Meng, and ShuZhou Wang. "Day-ahead electricity price prediction based on multiple ELM." In 2010 Chinese Control and Decision Conference (CCDC). IEEE, 2010. http://dx.doi.org/10.1109/ccdc.2010.5499079.
Повний текст джерелаHehui Qian and Zhiwei Qiu. "Feature selection using C4.5 algorithm for electricity price prediction." In 2014 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2014. http://dx.doi.org/10.1109/icmlc.2014.7009113.
Повний текст джерелаZhang, Shan, Powei Chen, and Ziqiang Yang. "Electricity price prediction based on a new hybrid model." In 2022 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS). IEEE, 2022. http://dx.doi.org/10.1109/tocs56154.2022.10015929.
Повний текст джерелаThan, Moh Moh, and Thandar Thein. "Electricity Price Prediction for Geographically Distributed Data Centers in Multi-Region Electricity Markets." In 2018 3rd International Conference on Computer and Communication Systems (ICCCS). IEEE, 2018. http://dx.doi.org/10.1109/ccoms.2018.8463272.
Повний текст джерелаAnamika and Niranjan Kumar. "Market Clearing Price prediction using ANN in Indian Electricity Markets." In 2016 International Conference on Energy Efficient Technologies for Sustainability (ICEETS). IEEE, 2016. http://dx.doi.org/10.1109/iceets.2016.7583797.
Повний текст джерелаTian, Huixin, Mu Zhang, and Bo Meng. "Prediction of day-ahead electricity price based on information fusion." In 2010 International Conference on Computer and Information Application (ICCIA). IEEE, 2010. http://dx.doi.org/10.1109/iccia.2010.6141635.
Повний текст джерелаRawal, Keerti, and Aijaz Ahmad. "Day-Ahead Market Electricity Price Prediction using Time Series Forecasting." In 2022 1st International Conference on Sustainable Technology for Power and Energy Systems (STPES). IEEE, 2022. http://dx.doi.org/10.1109/stpes54845.2022.10006455.
Повний текст джерелаYan, Chenyu, Chaochao Qu, and Gao Mo. "Short-term Electricity Price Prediction Based on CEEMD-TCN-ATTENTION." In 2022 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS). IEEE, 2022. http://dx.doi.org/10.1109/tocs56154.2022.10015959.
Повний текст джерелаЗвіти організацій з теми "Electricity price prediction"
Muelaner, Jody Emlyn. Unsettled Issues in Electrical Demand for Automotive Electrification Pathways. SAE International, January 2021. http://dx.doi.org/10.4271/epr2021004.
Повний текст джерела