Relevant bibliographies by topics / Electricity price prediction

Academic literature on the topic 'Electricity price prediction'

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Published: 14 March 2023

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Journal articles on the topic "Electricity price prediction"

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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.

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The electricity market is a complex, evolutionary, and dynamic environment. Forecasting electricity prices is an important issue for all electricity market participants. In this study, we shed light on how to improve electricity price forecasting accuracy through the use of a machine learning technique—namely, a novel genetic programming approach. Drawing on empirical data from the largest EU energy markets, we propose a forecasting model that considers variables related to weather conditions, oil prices, and CO2 coupons and predicts energy prices 24 h ahead. We show that the proposed model provides more accurate predictions of future electricity prices than existing prediction methods. Our important findings will assist the electricity market participants in forecasting future price movements.
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Cao, 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.

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With the reform of electric power system, major progress has been made in the construction of the electricity market. Electricity prices are a key influencing factor in the electricity market, and each participant trades electricity based on the price of electricity. Therefore, improving the accuracy of electricity price forecasts is important for every player in the electricity market. Prediction using single-layer neural networks has limited accuracy. Due to the high accuracy of machine learning in forecasting, the method of deep belief network is used to predict the price of electricity in the future. Real data from the U.S. PJM electricity market are used for simulation and compared with the prediction models of other neural networks. The results show that the prediction accuracy of the deep belief network model is higher, and the use of the deep belief network can provide an effective method for China’s electricity sales companies to predict electricity prices.
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Xie, 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.

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The price of electricity is an important factor in the electricity market. Accurate electricity price forecasting (EPF) is very important to all competing electricity market parties. Decision-making in the electricity market is highly dependent on electricity prices, making an EPF model an important part of the orderly and efficient operation of the electricity market. Especially during the COVID-19 pandemic, the prices of raw materials for electricity production, such as coal, have risen sharply. Forecasting electricity prices has become particularly important. Currently, existing EPF prediction models face two main challenges: First, how to integrate multiscale electricity price data to obtain a higher prediction accuracy. Second, how to solve the problem of data noise caused by the fusion of EPF samples and multiscale data. To solve the above problems, we innovatively propose a tensor decomposition method to integrate multiscale electricity price data and use L1 regularization and wavelet transform to remove data noise. In general, this paper proposes a deep learning EPF prediction model, named the WT_TDLSTM model, based on tensor decomposition, a wavelet transform, and long short-term memory (LSTM). Among them, the LSTM method is used to predict electricity prices. We conducted experiments on three datasets. The experimental results of three data prove that the WT_TDLSTM model is better than the compared EPF model. The indicators of MSE and RMSE are 33.65–99.97% better than the comparison model. We believe that the WT_TDLSTM model is a good supplement to the EPF model.
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Arvanitidis, 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.

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In recent decades, the traditional monopolistic energy exchange market has been replaced by deregulated, competitive marketplaces in which electricity may be purchased and sold at market prices like any other commodity. As a result, the deregulation of the electricity industry has produced a demand for wholesale organized marketplaces. Price predictions, which are primarily meant to establish the market clearing price, have become a significant factor to an energy company’s decision making and strategic development. Recently, the fast development of deep learning algorithms, as well as the deployment of front-end metaheuristic optimization approaches, have resulted in the efficient development of enhanced prediction models that are used for electricity price forecasting. In this paper, the development of six highly accurate, robust and optimized data-driven forecasting models in conjunction with an optimized Variational Mode Decomposition method and the K-Means clustering algorithm for short-term electricity price forecasting is proposed. In this work, we also establish an Inverted and Discrete Particle Swarm Optimization approach that is implemented for the optimization of the Variational Mode Decomposition method. The prediction of the day-ahead electricity prices is based on historical weather and price data of the deregulated Greek electricity market. The resulting forecasting outcomes are thoroughly compared in order to address which of the two proposed divide-and-conquer preprocessing approaches results in more accuracy concerning the issue of short-term electricity price forecasting. Finally, the proposed technique that produces the smallest error in the electricity price forecasting is based on Variational Mode Decomposition, which is optimized through the proposed variation of Particle Swarm Optimization, with a mean absolute percentage error value of 6.15%.
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Xie, 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.

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More and more IoT (Internet of Thing) devices have been connected to our lives in recent years, making life more convenient. Many countries are also making use of Internet of Thing technology to carry out intelligent electricity network reform. One of the reform goals is balancing the supply and demand of electricity, which has become a top priority. Balancing electricity supply and demand through real-time electricity prices has become an effective way. However, using traditional machine learning models for real-time electricity price prediction requires complex feature engineering, and the results are not satisfactory. Also, the mainstream fusion methods use data-level fusion, which will put very high pressure on communication bandwidth and computer resources. In this paper, an LSTM- (long short-term memory-) based decision level fusion of multisource data is proposed and applied for real-time electricity price prediction on actual electricity price datasets. The method solves the difficulties of traditional machine learning models in dealing with complex nonlinear problems. It achieves local asynchronous processing of multisource data through decision-level fusion, reducing the requirement for bandwidth resources and providing perfect results in real-time electricity price prediction. The experimental results show that the prediction accuracy of the decision fusion prediction model based on LSTM is higher than that of the linear regression algorithm.
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Asemota, 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.

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The shortage of local electricity generation capacity coupled with increasing reliance on South Africa, from which it imports about forty-eight (48%) percent of its electricity, and another five (5%) percent from Zambia, Zimbabwe and other short term energy markets constitute the major shortcomings of electricity industry in Namibia.Therefore, price stability and volatility indices of electricity can directly impact on the developmental imperatives of any nation. This is so because the quality, quantity and pricing of electricity available to the citizenry have become the common denominators for measuring the standards of living of any commune, like Namibia. Extensive literature searchand review, and about 127 yielded questionnaires out of the 300 administered questionnaires; were used to gather data for the study. The yielded survey data were subsequently subjected to statistical analyses using the Statistical Package for Social Sciences (SPSS version 11.5) to develop a sigmoid plot for predicting the future electricity pricing model for Namibia employing first order differential equations. The results show that the generalisedlogistic equation model for the future pricing of electricity consumed in Namibia, increased by about 13.52% per year. Upon substituting the available 1995 electricity pricing data into the logistic equation model, it was possible to predict the future electricity price for 2010, with about 1.8% error. It can be seen that the developed logistic model fit is only viable for about fifteen (15) years. It is suggested that, better estimates can be obtained if the median electricity price for either 2002 or 2003 is used as the initial electricity price, to obtain more credible electricity prices with longertime ranges, for Namibia.
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Wan 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.

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Predicting the price of electricity is crucial for the operation of power systems. Short-term electricity price forecasting deals with forecasts from an hour to a day ahead. Hourly-ahead forecasts offer expected prices to market participants before operation hours. This is especially useful for effective bidding strategies where the bidding amount can be reviewed or changed before the operation hours. Nevertheless, many existing models have relatively low prediction accuracy. Furthermore, single prediction models are typically less accurate for different scenarios. Thus, a hybrid model comprising least squares support vector machine (LSSVM) and genetic algorithm (GA) was developed in this work to predict electricity prices with higher accuracy. This model was tested on the Ontario electricity market. The inputs, which were the hourly Ontario electricity price (HOEP) and demand for the previous seven days, as well as 1-h pre-dispatch price (PDP), were optimized by GA to prevent losing potentially important inputs. At the same time, the LSSVM parameters were optimized by GA to obtain accurate forecasts. The hybrid LSSVM-GA model was shown to produce an average mean absolute percentage error (MAPE) of 8.13% and the structure of this model is less complex compared with other models developed in previous studies. This is due to the fact that only two algorithms were used (LSSVM and GA), with the load and HOEP for the week preceding the forecasting hour as the inputs. Based on the results, it is concluded that the proposed hybrid algorithm is a promising alternative to produce good electricity price forecasts.
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Oksuz, 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.

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The intraday electricity markets are continuous trade platforms for each hour of the day and have specific characteristics. These markets have shown an increasing number of transactions due to the requirement of close to delivery electricity trade. Recently, intraday electricity price market research has seen a rapid increase in a number of works for price prediction. However, most of these works focus on the features and descriptive statistics of the intraday electricity markets and overlook the comparison of different available models. In this paper, we compare a variety of methods including neural networks to predict intraday electricity market prices in Turkish intraday market. The recurrent neural networks methods outperform the classical methods. Furthermore, gated recurrent unit network architecture achieves the best results with a mean absolute error of 0.978 and a root mean square error of 1.302. Moreover, our results indicate that day-ahead market price of the corresponding hour is a key feature for intraday price forecasting and estimating spread values with day-ahead prices proves to be a more efficient method for prediction.
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Zhang, 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.

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In an open electricity market, increased accuracy and real-time availability of electricity price forecasts can help market parties participate effectively in market operations and management. As the penetration of clean energy increases, it brings new challenges to electricity price forecasting. An electricity price forecasting model is constructed in this paper for markets containing a high proportion of wind and solar power, where the scenario with a high coefficient of variation (COV) caused by the high frequency of low electricity prices is particularly concerned. The deep extreme learning machine optimized by the sparrow search algorithm (SSA-DELM) is proposed to make predictions on the model. The results show that wind–load ratio and solar–load ratio are the key input variables for forecasting in power markets with high proportions of wind and solar energy. The SSA-DELM possesses better electricity price forecasting performance in the scenario with a high COV and is more suitable for disordered time series models, which can be confirmed in comparison with LSTM.
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Lu, 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.

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The construction of grid plays an important role in national economic development, social stability and peoples life. In case that electricity market adopts real time electricity price, users active participation and real time response to electricity price will change the traditional load prediction from rigid forecasting to flexible forecasting which takes electricity demand response into consideration. By using wavelet analysis and error characteristics analysis, the researches into the probabilistic predicting method for demand changes under the real time electricity pricing is carried out. The probabilistic load prediction result shall enable decision makers to better understand the load change range in the future and make more reasonable decision. Meanwhile, it shall provide support to electricity system risk analysis.
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Dissertations / Theses on the topic "Electricity price prediction"

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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.

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Chen, Jie. "Theoretical Results and Applications Related to Dimension Reduction." Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/19841.

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To overcome the curse of dimensionality, dimension reduction is important and necessary for understanding the underlying phenomena in a variety of fields. Dimension reduction is the transformation of high-dimensional data into a meaningful representation in the low-dimensional space. It can be further classified into feature selection and feature extraction. In this thesis, which is composed of four projects, the first two focus on feature selection, and the last two concentrate on feature extraction. The content of the thesis is as follows. The first project presents several efficient methods for the sparse representation of a multiple measurement vector (MMV); some theoretical properties of the algorithms are also discussed. The second project introduces the NP-hardness problem for penalized likelihood estimators, including penalized least squares estimators, penalized least absolute deviation regression and penalized support vector machines. The third project focuses on the application of manifold learning in the analysis and prediction of 24-hour electricity price curves. The last project proposes a new hessian regularized nonlinear time-series model for prediction in time series.
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Ruthberg, 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.

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Introducing more intermittent renewable energy sources in the energy system makes the role of balancing power more important. Furthermore, an increased infeed from intermittent renewable energy sources also has the effect of creating lower and more volatile electricity prices. Hence, investing in balancing power is prone to high risks with respect to expected profits, which is why a good representation of electricity prices is vital in order to motivate future investments. We propose a stochastic multi-factor model to be used for simulating the long-run dynamics of electricity prices as input to investment valuation of power generation assets. In particular, the proposed model is used to assess the impact of electricity price dynamics on investment decisions with respect to balancing power generation, where a combined heat and power plant is studied in detail. Since the main goal of the framework is to create a long-term representation of electricity prices so that the distributional characteristics of electricity prices are maintained, commonly cited as seasonality, mean reversion and spikes, the model is evaluated in terms of yearly duration which describes the distribution of electricity prices over time. The core aspects of the framework are derived from the mean-reverting Pilipovic model of commodity prices, but where we extend the assumptions in a multi-factor framework by adding a functional link to the supply- and demand for power as well as outdoor temperature. On average, using the proposed model as a way to represent future prices yields a maximum 9 percent overand underprediction of duration respectively, a result far better than those obtained by simpler models such as a seasonal profile or mean estimates which do not incorporate the full characteristics of electricity prices. Using the different aspects of the model, we show that variations of electricity prices have a large impact on the investment decision with respect to balancing power. The realized value of the flexibility to produce electricity in a combined heat and power plant is calculated, which yields a valuation close to historical realized values. Compared with simpler models, this is a significant improvement. Finally, we show that by including characteristics such as non-constant volatility and spiky behavior in investment decisions, the expected value of balancing power generators, such as combined heat and power plants, increases.
I 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.
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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.

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This thesis deals with the new flow-based computation method used in the Central Western Europe Area. This is done on the financial side. The main aim is to produce some robust methods for predicting. Two approaches are used: the first one is based on a deterministic and algorithmic method involving the study of the interaction between the fundamentals and the prices. The other one is a more statistical approach based on a time series modeling of the French flow-based prices. Both approaches have advantages and disadvantages which will be discussed in the following. The work is mainly based on global simulated data provided by CASC in their implementation phase of the flow-base in Western Europe.
Denna 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.
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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.

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As a result of deregulation of most power markets around the world electricity price modeling and forecasting have obtained increasing importance in recent years. Large number of models has been studied on a wide range of power markets, from linear time series and multivariate regression models to more complex non linear models with jumps, but results are mixing and there is no single model that provides convincing superior performance in forecasting spot prices. This study considers whether combination forecasts of spot electricity prices are statistically superior to a wide range of single model based forecasts. To this end we focus on one-day ahead forecasting of half-hourly spot data from the British UK Power Exchange electricity market. In this work we focus on modeling data corresponding to some load periods of the day in order to evaluate the forecasting performance of prices representative of different moment of the day. Several forecasting models for power spot prices are estimated on the basis of expanding and/or rolling estimation windows of different sizes. Included are linear ARMAX models, different specifications of multiple regression models, non linear Markov switching regression models and time-varying parameter regression models. One-day ahead forecasts are obtained for each model and evaluated according to different statistical criteria as prediction error statistics and the Diebold and Mariano test for equal predictive accuracy. Forecasting results highlight that no model globally outperforms the others: differences in forecasting accuracy depend on several factors, such as model specification, sample realization and forecasting period. Since different forecasting models seem to capture different features of spot price dynamics, we propose a forecasting approach based on the combination of forecasts. This approach has been useful to improve forecasting accuracy in several empirical situations, but it is novel in the spot electricity price forecasting context. In this work different strategies have been employed to construct combination forecasts. The simplest approach is an equally weighted combination of the forecasts. An alternative is the use of adaptive forecast combination procedures, which allows for time-varying combination coefficients. Methods from Bates & Granger (1969) are considered. Models entering the combination are chosen for each forecasting season using the model confidence set method (MCS) described in Hansen et al. (2003, 2005) and then screened with the forecasts encompassing method of Fair & Shiller (1990). For each load period, our findings underline that models behave differently in each season. For this reason we propose a combination applied at a seasonal level. In this thesis some promising results in this direction are presented. The combination results are compared with the best results obtained from the single models in each forecasting period and for different prediction error statistics. Our findings illustrate the usefulness of the procedure, showing that combining forecasts at a seasonal level have the potential to produce predictions of superior or equal accuracy relative to the individual forecasts.
Con 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.
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Januário, João Filipe Ferreira. "Electricity price forecasting utilizing machine learning in MIBEL." Master's thesis, 2019. http://hdl.handle.net/10071/20235.

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Short term electricity price forecasts have become increasingly important in the last few decades due to the rise of more competitive electricity markets throughout the globe. Accurate forecasts are now essential for market players to maximize their profits and hedge against risk, hence various forecasting methodologies have been applied to electricity price forecasting in the last few decades. This dissertation explores the main methodologies and how accurately can three popular machine learning models, SVR LSTM and XGBoost, predict prices in the Iberian market of electricity. Additionally, a study on input variables and their relationship with the final price is made.
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Alves, 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.

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Classificação: C01, C02, C63, G17, Q41 Q47
Com 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.
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Book chapters on the topic "Electricity price prediction"

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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.

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Pao, 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.

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Vardakas, 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.

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Shuja, 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.

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Xu, 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.

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Sinha, 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.

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Scholz, 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.

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Huang, 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.

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Sanchez, 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.

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In this chapter, we propose the use of Higher Order Neural Networks (HONNs) trained with an extended Kalman filter based algorithm to predict the electric load demand as well as the electricity prices, with beyond a horizon of 24 hours. Due to the chaotic behavior of the electrical markets, it is not advisable to apply the traditional forecasting techniques used for time series; the results presented here confirm that HONNs can very well capture the complexity underlying electric load demand and electricity prices. The proposed neural network model produces very accurate next day predictions and also, prognosticates with very good accuracy, a week-ahead demand and price forecasts.
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Fainti, 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.

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The modern way of living depends on a very high degree on electricity utilization. People take for granted that their energy needs will be satisfied 24/7 which mandates the maintaining of the power grid in stable state. To that end, the development of precise methods for monitoring and predicting events that might disturb its uninterrupted operation is immense. Moreover, the evolvement of power grids into smart grids where the end users continuously participate in the power market by forming energy prices and/or by adjusting their energy needs according to their own agenda, adds high volatility to load demand. In that sense, with regard to predictive methods, a plain single point prediction application may not be enough. The aim of this study is to develop and evaluate a method in order to further enhance this type of applications by providing Predictive Intervals (PIs) regarding ampacity overloading in smart power systems through the use of Artificial Neural Networks (ANNs).
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Conference papers on the topic "Electricity price prediction"

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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.

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Deng, 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.

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Huixin 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.

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Hehui 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.

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Zhang, 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.

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Than, 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.

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Anamika 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.

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Tian, 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.

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Rawal, 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.

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Yan, 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.

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Reports on the topic "Electricity price prediction"

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Muelaner, Jody Emlyn. Unsettled Issues in Electrical Demand for Automotive Electrification Pathways. SAE International, January 2021. http://dx.doi.org/10.4271/epr2021004.

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With the current state of automotive electrification, predicting which electrification pathway is likely to be the most economical over a 10- to 30-year outlook is wrought with uncertainty. The development of a range of technologies should continue, including statically charged battery electric vehicles (BEVs), fuel cell electric vehicles (FCEVs), plug-in hybrid electric vehicles (PHEVs), and EVs designed for a combination of plug-in and electric road system (ERS) supply. The most significant uncertainties are for the costs related to hydrogen supply, electrical supply, and battery life. This greatly is dependent on electrolyzers, fuel-cell costs, life spans and efficiencies, distribution and storage, and the price of renewable electricity. Green hydrogen will also be required as an industrial feedstock for difficult-to-decarbonize areas such as aviation and steel production, and for seasonal energy buffering in the grid. For ERSs, it is critical to understand how battery life will be affected by frequent cycling and the extent to which battery technology from hybrid vehicles can be applied. Unsettled Issues in Electrical Demand for Automotive Electrification Pathways dives into the most critical issues the mobility industry is facing.
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