Journal articles on the topic 'Electricity price prediction'
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1
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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Tabassum, Zahira, and B. S. Chandrasekar Shastry. "Short Term Load Forecasting of Residential and Commercial Consumers of Karnataka Electricity Board using CFNN." International Journal of Electrical and Electronics Research 10, no. 2 (June 30, 2022): 347–52. http://dx.doi.org/10.37391/ijeer.100247.
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Electricity use and its access are correlated in the economic development of any country. Economically, electricity cannot be stored, and for stability of an electrical network a balance between generation and consumption is necessary. Electricity demand depends on various factors like temperature, everyday activities, time of day, days of the week days/Holidays. These parameters have led to price volatility and huge spikes in electricity prices. The research work proposes a short term Load prediction Model for LT2 (residential consumers), LT3 (Commercial Consumers) of Karnataka State Electricity Board using Cascaded Feed Forward Neural Network (CFNN). MATLAB software is utilized to design and test the forecasting model for predicting the power consumption. Furthermore, a shallow feed forward neural network-based prediction model is constructed and evaluated for performance comparison. The Performance metrics include Mean Absolute Percentage Error (MAPE) and Mean Squared Error (MSE). The suggested STLF CFNN prediction model outperformed shallow feed forward networks on both performance metrics with prediction errors of less than 1%.
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Guo, Fang, Shangyun Deng, Weijia Zheng, An Wen, Jinfeng Du, Guangshan Huang, and Ruiyang Wang. "Short-Term Electricity Price Forecasting Based on the Two-Layer VMD Decomposition Technique and SSA-LSTM." Energies 15, no. 22 (November 11, 2022): 8445. http://dx.doi.org/10.3390/en15228445.
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Accurate electricity price forecasting (EPF) can provide a necessary basis for market decision making by power market participants to reduce the operating cost of the power system and ensure the system’s stable operation. To address the characteristics of high frequency, strong nonlinearity, and high volatility of electricity prices, this paper proposes a short-term electricity price forecasting model based on a two-layer variational modal decomposition (VMD) technique, using the sparrow search algorithm (SSA) to optimize the long and short-term memory network (LSTM). The original electricity price sequence is decomposed into multiple modal components using VMD. Then, each piece is predicted separately using an SSA-optimized LSTM. For the element with the worst prediction accuracy, IMF-worst is decomposed for a second time using VMD to explore the price characteristics further. Finally, the prediction results of each modal component are reconstructed to obtain the final prediction results. To verify the validity and accuracy of the proposed model, this paper uses data from three electricity markets, Australia, Spain, and France, for validation analysis. The experimental results show that the proposed model has MAPE of 0.39%, 1.58%, and 0.95%, RMSE of 0.25, 0.9, and 0.3, and MAE of 0.19, 0.68, and 0.31 in three different cases, indicating that the proposed model can well handle the nonlinear and non-stationarity characteristics of the electricity price series and has superior forecasting performance.
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Ahrazem Dfuf, Ismael, José Mira McWilliams, and María González Fernández. "Multi-Output Conditional Inference Trees Applied to the Electricity Market: Variable Importance Analysis." Energies 12, no. 6 (March 21, 2019): 1097. http://dx.doi.org/10.3390/en12061097.
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Predicting electricity prices and demand is a very important issue for the energy market industry. In order to improve the accuracy of any predictive model, a previous variable importance analysis is highly advised. In this paper, we propose an alternative framework to assess the variable importance in multivariate response scenarios based on the permutation importance technique, applying the Conditional inference trees algorithm and a ϕ -divergence measure. Our solution was tested in simulated examples as well as a real case, where we assessed and ranked the most relevant predictors for price and demand of electricity jointly in the Spanish market. The new method outperforms, in most cases, the outcomes achieved by the recently proposed techniques, Intervention prediction measure (IPM) and Sequential multi-response feature selection (SMuRFS). For the electricity market case, we identified the most relevant predictors among pollutant, renewable, calendar and lagged prices variables for the joint response of demand and price, showing also the effectiveness of the proposed multivariate response method when compared with the univariate response analysis.
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Chen, Yiyuan, Yufeng Wang, Jianhua Ma, and Qun Jin. "BRIM: An Accurate Electricity Spot Price Prediction Scheme-Based Bidirectional Recurrent Neural Network and Integrated Market." Energies 12, no. 12 (June 12, 2019): 2241. http://dx.doi.org/10.3390/en12122241.
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For the benefit from accurate electricity price forecasting, not only can various electricity market stakeholders make proper decisions to gain profit in a competitive environment, but also power system stability can be improved. Nevertheless, because of the high volatility and uncertainty, it is an essential challenge to accurately forecast the electricity price. Considering that recurrent neural networks (RNNs) are suitable for processing time series data, in this paper, we propose a bidirectional long short-term memory (LSTM)-based forecasting model, BRIM, which splits the state neurons of a regular RNN into two parts: the forward states (using the historical electricity price information) are designed for processing the data in positive time direction and backward states (using the future price information available at inter-connected markets) for the data in negative time direction. Moreover, due to the fact that inter-connected power exchange markets show a common trend for other neighboring markets and can provide signaling information for each other, it is sensible to incorporate and exploit the impact of the neighboring markets on forecasting accuracy of electricity price. Specifically, future electricity prices of the interconnected market are utilized both as input features for forward LSTM and backward LSTM. By testing on day-ahead electricity prices in the European Power Exchange (EPEX), the experimental results show the superiority of the proposed method BRIM in enhancing predictive accuracy in comparison with the various benchmarks, and moreover Diebold-Mariano (DM) shows that the forecast accuracy of BRIM is not equal to other forecasting models, and thus indirectly demonstrates that BRIM statistically significantly outperforms other schemes.
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Shikhina, Anna V., and Tatyana V. Yagodkina. "Improving the Electricity Price Prediction Accuracy by Applying Combined Prediction Models." Vestnik MEI 6, no. 6 (2020): 119–28. http://dx.doi.org/10.24160/1993-6982-2020-6-119-128.
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The solution of problems concerned with predicting a free market price for electricity through constructing different prediction models is considered. In so doing, a shift is made from an analysis of conventional regression and auto-regression models of the moving average to the proposed combined multifactor models, which also include the time trend and dummy variables. This shift is partly justified by the specific behavior of the electricity price in the free market, which is caused by a strictly cyclic change of its value, e.g., proceeding from such attributes as the heating season, day of week, etc. The techniques of constructing combined prediction models has been developed to the level of elaborating effective computational procedures based on the Statistica and OsiSoft PI-System software packages. The application of the autoregressive and combined regression prediction models to the Russian market has demonstrated their fairly good effectiveness with an acceptable level of accuracy. A comparison of the achieved levels of accuracy provided by the competing models has not shown any advantages of the shift to the use of combined regression multifactor models in terms of achieving better prediction accuracy; however, their application for analyzing the influence of different factors on the predicted variable may become a fundamental advantage in selecting the type of prediction model. Despite their being limited to an analysis of the Belgorod region market, the obtained results demonstrate the achieved prediction accuracy that is as least as good, and in the main is even better than the majority of the data presented in the review of the results for European electricity markets. The article substantiates the advisability of studying the combined regression models as a tool for analyzing the influence of individual factors as components influencing the electricity price formation for the predicted period, given that the accuracy level of the combined regression models corresponds to the currently achieved electricity price prediction accuracy levels.
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Vega-Márquez, Belén, Cristina Rubio-Escudero, Isabel A. Nepomuceno-Chamorro, and Ángel Arcos-Vargas. "Use of Deep Learning Architectures for Day-Ahead Electricity Price Forecasting over Different Time Periods in the Spanish Electricity Market." Applied Sciences 11, no. 13 (June 30, 2021): 6097. http://dx.doi.org/10.3390/app11136097.
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The importance of electricity in people’s daily lives has made it an indispensable commodity in society. In electricity market, the price of electricity is the most important factor for each of those involved in it, therefore, the prediction of the electricity price has been an essential and very important task for all the agents involved in the purchase and sale of this good. The main problem within the electricity market is that prediction is an arduous and difficult task, due to the large number of factors involved, the non-linearity, non-seasonality and volatility of the price over time. Data Science methods have proven to be a great tool to capture these difficulties and to be able to give a reliable prediction using only price data, i.e., taking the problem from an univariate point of view in order to help market agents. In this work, we have made a comparison among known models in the literature, focusing on Deep Learning architectures by making an extensive tuning of parameters using data from the Spanish electricity market. Three different time periods have been used in order to carry out an extensive comparison among them. The results obtained have shown, on the one hand, that Deep Learning models are quite effective in predicting the price of electricity and, on the other hand, that the different time periods and their particular characteristics directly influence the final results of the models.
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Wan, Can, Ming Niu, Yonghua Song, and Zhao Xu. "Pareto Optimal Prediction Intervals of Electricity Price." IEEE Transactions on Power Systems 32, no. 1 (January 2017): 817–19. http://dx.doi.org/10.1109/tpwrs.2016.2550867.
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Ertuğrul, Hasan Murat, Mustafa Tevfik Kartal, Serpil Kılıç Depren, and Uğur Soytaş. "Determinants of Electricity Prices in Turkey: An Application of Machine Learning and Time Series Models." Energies 15, no. 20 (October 12, 2022): 7512. http://dx.doi.org/10.3390/en15207512.
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The study compares the prediction performance of alternative machine learning algorithms and time series econometric models for daily Turkish electricity prices and defines the determinants of electricity prices by considering seven global, national, and electricity-related variables as well as the COVID-19 pandemic. Daily data that consist of the pre-pandemic (15 February 2019–10 March 2020) and the pandemic (11 March 2020–31 March 2021) periods are included. Moreover, various time series econometric models and machine learning algorithms are applied. The findings reveal that (i) machine learning algorithms present higher prediction performance than time series models for both periods, (ii) renewable sources are the most influential factor for the electricity prices, and (iii) the COVID-19 pandemic caused a change in the importance order of influential factors on the electricity prices. Thus, the empirical results highlight the consideration of machine learning algorithms in electricity price prediction. Based on the empirical results obtained, potential policy implications are also discussed.
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Liu, Yali, Tingting Chai, Zhaoxin Zhang, and Gang Long. "Towards Electricity Price and Electric Load Forecasting Using Multi-task Deep Learning." Journal of Physics: Conference Series 2171, no. 1 (January 1, 2022): 012048. http://dx.doi.org/10.1088/1742-6596/2171/1/012048.
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Abstract The continuous development of the power Internet of Things (IOT) has enabled power market participants to obtain a large amount of data. Simultaneously, the power IOT has an increasing demand for power load and electricity price forecasting; Since the forecasting of electricity load and electricity price is a single task, and the model calculation accuracy is not high, this brings great challenges to the accurate forecasting of electricity load and electricity price. In this paper, two power load and electricity price forecasting models via multi-task deep learning are established perform high-precision joint forecasting of power load and electricity price Experimental results demonstrate that the prediction results of the proposed deep learning models are superior to the other compared approaches in terms of the main task and the auxiliary task, and show superior prediction performance, verifying the practicability and superiority of the power load and electricity price multi-task forecasting model.
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Kostrzewski, Maciej, and Jadwiga Kostrzewska. "The Impact of Forecasting Jumps on Forecasting Electricity Prices." Energies 14, no. 2 (January 9, 2021): 336. http://dx.doi.org/10.3390/en14020336.
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The paper is devoted to forecasting hourly day-ahead electricity prices from the perspective of the existence of jumps. We compare the results of different jump detection techniques and identify common features of electricity price jumps. We apply the jump-diffusion model with a double exponential distribution of jump sizes and explanatory variables. In order to improve the accuracy of electricity price forecasts, we take into account the time-varying intensity of price jump occurrences. We forecast moments of jump occurrences depending on several factors, including seasonality and weather conditions, by means of the generalised ordered logit model. The study is conducted on the basis of data from the Nord Pool power market. The empirical results indicate that the model with the time-varying intensity of jumps and a mechanism of jump prediction is useful in forecasting electricity prices for peak hours, i.e., including the probabilities of downward, no or upward jump occurrences into the model improves the forecasts of electricity prices.
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Kostrzewski, Maciej, and Jadwiga Kostrzewska. "The Impact of Forecasting Jumps on Forecasting Electricity Prices." Energies 14, no. 2 (January 9, 2021): 336. http://dx.doi.org/10.3390/en14020336.
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The paper is devoted to forecasting hourly day-ahead electricity prices from the perspective of the existence of jumps. We compare the results of different jump detection techniques and identify common features of electricity price jumps. We apply the jump-diffusion model with a double exponential distribution of jump sizes and explanatory variables. In order to improve the accuracy of electricity price forecasts, we take into account the time-varying intensity of price jump occurrences. We forecast moments of jump occurrences depending on several factors, including seasonality and weather conditions, by means of the generalised ordered logit model. The study is conducted on the basis of data from the Nord Pool power market. The empirical results indicate that the model with the time-varying intensity of jumps and a mechanism of jump prediction is useful in forecasting electricity prices for peak hours, i.e., including the probabilities of downward, no or upward jump occurrences into the model improves the forecasts of electricity prices.
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Pourhaji, Nazila, Mohammad Asadpour, Ali Ahmadian, and Ali Elkamel. "The Investigation of Monthly/Seasonal Data Clustering Impact on Short-Term Electricity Price Forecasting Accuracy: Ontario Province Case Study." Sustainability 14, no. 5 (March 6, 2022): 3063. http://dx.doi.org/10.3390/su14053063.
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The transformation of the electricity market structure from a monopoly model to a competitive market has caused electricity to be exchanged like a commercial commodity in the electricity market. The electricity price participants should forecast the price in different horizons to make an optimal offer as a buyer or a seller. Therefore, accurate electricity price prediction is very important for market participants. This paper investigates the monthly/seasonal data clustering impact on price forecasting. To this end, after clustering the data, the effective parameters in the electricity price forecasting problem are selected using a grey correlation analysis method and the parameters with a low degree of correlation are removed. At the end, the long short-term memory neural network has been implemented to predict the electricity price for the next day. The proposed method is implemented on Ontario—Canada data and the prediction results are compared in three modes, including non-clustering, seasonal, and monthly clustering. The studies show that the prediction error in the monthly clustering mode has decreased compared to the non-clustering and seasonal clustering modes in two different values of the correlation coefficient, 0.5 and 0.6.
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Yoo, Shi Yong. "The Valuation of the Electricity Future Contract Under Weather Uncertainty." Journal of Derivatives and Quantitative Studies 12, no. 2 (November 30, 2004): 127–55. http://dx.doi.org/10.1108/jdqs-02-2004-b0006.
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This paper is concerned with the effects of weather uncertainty on the electricity future curve. Following the approach used by Lucia and Schwartz (2002), the behavior of the underlying spot price is assumed to consist of two components ‘ a totally predictable deterministic component that accounts for regularities in the evolution of prices and a stochastic component that accounts for the behavior of residuals from the deterministic part. The weather uncertainty is modeled consistently with seasonal outlook probabilities from the CPC (Climate Prediction Center) outlook. For a given realization of temperature, the electricity load can be predicted very accurately by a time series model using temperature and other explanatory variables. Furthermore, if temperature and electricity load are known, the spot price can be predicted as well using the regime switching model with time-varying transition probabilities. The electricity future price can be calculated for the given seasonal probabilities from the CPC outlook. Then the electricity future price can be obtained as the arithmetic average of the one-day electricity future price. The future price reflects clearly the response of the spot price to different weather patterns. As the summer gets warmer, the high price regime is more likely to be realized, and as a result, the future price increases.
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Tashpulatov, Sherzod N. "The Impact of Regulatory Reforms on Demand Weighted Average Prices." Mathematics 9, no. 10 (May 14, 2021): 1112. http://dx.doi.org/10.3390/math9101112.
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Average prices are popularly used in the literature on price modeling. Calculating daily or weekly prices as an average over hourly or half-hourly trading periods assumes the same weight ignoring demand or traded volumes during those periods. Analyzing demand weighted average prices is important if producers may affect prices by decreasing them during low-demand periods and increasing them during high-demand periods within a day. The prediction of this price manipulation might have motivated the regulatory authority to introduce price caps not only on annual average prices but also on annual demand weighted average prices in the England and Wales wholesale electricity market. The dynamics of demand weighted average prices of electricity has been analyzed little in the literature. We show that skew generalized error distribution (SGED) is the appropriate assumption for model residuals. The estimated volatility model is used for evaluating the impact of regulatory reforms on demand weighted average prices during the complete history of the England and Wales wholesale electricity market.
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Kahawala, Sachin, Daswin De Silva, Seppo Sierla, Damminda Alahakoon, Rashmika Nawaratne, Evgeny Osipov, Andrew Jennings, and Valeriy Vyatkin. "Robust Multi-Step Predictor for Electricity Markets with Real-Time Pricing." Energies 14, no. 14 (July 20, 2021): 4378. http://dx.doi.org/10.3390/en14144378.
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Real-time electricity pricing mechanisms are emerging as a key component of the smart grid. However, prior work has not fully addressed the challenges of multi-step prediction (Predicting multiple time steps into the future) that is accurate, robust and real-time. This paper proposes a novel Artificial Intelligence-based approach, Robust Intelligent Price Prediction in Real-time (RIPPR), that overcomes these challenges. RIPPR utilizes Variational Mode Decomposition (VMD) to transform the spot price data stream into sub-series that are optimized for robustness using the Particle Swarm Optimization (PSO) algorithm. These sub-series are inputted to a Random Vector Functional Link neural network algorithm for real-time multi-step prediction. A mirror extension removal of VMD, including continuous and discrete spaces in the PSO, is a further novel contribution that improves the effectiveness of RIPPR. The superiority of the proposed RIPPR is demonstrated using three empirical studies of multi-step price prediction of the Australian electricity market.
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Domanski, Pawel D., and Mateusz Gintrowski. "Alternative approaches to the prediction of electricity prices." International Journal of Energy Sector Management 11, no. 1 (April 3, 2017): 3–27. http://dx.doi.org/10.1108/ijesm-06-2013-0001.
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Purpose This paper aims to present the results of the comparison between different approaches to the prediction of electricity prices. It is well-known that the properties of the data generation process may prefer some modeling methods over the others. The data having an origin in social or market processes are characterized by unexpectedly wide realization space resulting in the existence of the long tails in the probabilistic density function. These data may not be easy in time series prediction using standard approaches based on the normal distribution assumptions. The electricity prices on the deregulated market fall into this category. Design/methodology/approach The paper presents alternative approaches, i.e. memory-based prediction and fractal approach compared with established nonlinear method of neural networks. The appropriate interpretation of results is supported with the statistical data analysis and data conditioning. These algorithms have been applied to the problem of the energy price prediction on the deregulated electricity market with data from Polish and Austrian energy stock exchanges. Findings The first outcome of the analysis is that there are several situations in the task of time series prediction, when standard modeling approach based on the assumption that each change is independent of the last following random Gaussian bell pattern may not be a true. In this paper, such a case was considered: price data from energy markets. Electricity prices data are biased by the human nature. It is shown that more relevant for data properties was Cauchy probabilistic distribution. Results have shown that alternative approaches may be used and prediction for both data memory-based approach resulted in the best performance. Research limitations/implications “Personalization” of the model is crucial aspect in the whole methodology. All available knowledge should be used on the forecasted phenomenon and incorporate it into the model. In case of the memory-based modeling, it is a specific design of the history searching routine that uses the understanding of the process features. Importance should shift toward methodology structure design and algorithm customization and then to parameter estimation. Such modeling approach may be more descriptive for the user enabling understanding of the process and further iterative improvement in a continuous striving for perfection. Practical implications Memory-based modeling can be practically applied. These models have large potential that is worth to be exploited. One disadvantage of this modeling approach is large calculation effort connected with a need of constant evaluation of large data sets. It was shown that a graphics processing unit (GPU) approach through parallel calculation on the graphical cards can improve it dramatically. Social implications The modeling of the electricity prices has big impact of the daily operation of the electricity traders and distributors. From one side, appropriate modeling can improve performance mitigating risks associated with the process. Thus, the end users should receive higher quality of services ultimately with lower prices and minimized risk of the energy loss incidents. Originality/value The use of the alternative approaches, such as memory-based reasoning or fractals, is very rare in the field of the electricity price forecasting. Thus, it gives a new impact for further research enabling development of better solutions incorporating all available process knowledge and customized hybrid algorithms.
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Pavićević, Milutin, and Tomo Popović. "Forecasting Day-Ahead Electricity Metrics with Artificial Neural Networks." Sensors 22, no. 3 (January 28, 2022): 1051. http://dx.doi.org/10.3390/s22031051.
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As artificial neural network architectures grow increasingly more efficient in time-series prediction tasks, their use for day-ahead electricity price and demand prediction, a task with very specific rules and highly volatile dataset values, grows more attractive. Without a standardized way to compare the efficiency of algorithms and methods for forecasting electricity metrics, it is hard to have a good sense of the strengths and weaknesses of each approach. In this paper, we create models in several neural network architectures for predicting the electricity price on the HUPX market and electricity load in Montenegro and compare them to multiple neural network models on the same basis (using the same dataset and metrics). The results show the promising efficiency of neural networks in general for the task of short-term prediction in the field, with methods combining fully connected layers and recurrent neural or temporal convolutional layers performing the best. The feature extraction power of convolutional layers shows very promising results and recommends the further exploration of temporal convolutional networks in the field.
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Deng, Zhuofu, Xianglong Qi, Tengteng Xu, and Yingnan Zheng. "Operational Scheduling of Behind-the-Meter Storage Systems Based on Multiple Nonstationary Decomposition and Deep Convolutional Neural Network for Price Forecasting." Computational Intelligence and Neuroscience 2022 (February 21, 2022): 1–18. http://dx.doi.org/10.1155/2022/9326856.
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In the competitive electricity market, electricity price reflects the relationship between power supply and demand and plays an important role in the strategic behavior of market players. With the development of energy storage systems after watt-hour meter, accurate price prediction becomes more and more crucial in the energy management and control of energy storage systems. Due to the great uncertainty of electricity price, the performance of the general electricity price forecasting models is not satisfactory to be adopted in practice. Therefore, in this paper, we propose a novel electricity price forecasting strategy applied in optimization for the scheduling of battery energy storage systems. At first, multiple nonstationary decompositions are presented to extract the most significant components in price series, which express remarkably discriminative features in price fluctuation for regression prediction. In addition, all extracted components are delivered to a devised deep convolution neural network with multiscale dilated kernels for multistep price forecasting. At last, more advanced price fluctuation detection serves the optimized operation of the battery energy storage system within Ontario grid-connected microgrids. Sufficient ablation studies showed that our proposed price forecasting strategy provides predominant performances compared with the state-of-the-art methods and implies a promising prospect in economic benefits of battery energy storage systems.
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Sheha, Moataz, and Kody Powell. "Using Real-Time Electricity Prices to Leverage Electrical Energy Storage and Flexible Loads in a Smart Grid Environment Utilizing Machine Learning Techniques." Processes 7, no. 12 (November 21, 2019): 870. http://dx.doi.org/10.3390/pr7120870.
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With exposure to real-time market pricing structures, consumers would be incentivized to invest in electrical energy storage systems and smart predictive automation of their home energy systems. Smart home automation through optimizing HVAC (heating, ventilation, and air conditioning) temperature set points, along with distributed energy storage, could be utilized in the process of optimizing the operation of the electric grid. Using electricity prices as decision variables to leverage electrical energy storage and flexible loads can be a valuable tool to optimize the performance of the power grid and reduce electricity costs both on the supply and demand sides. Energy demand prediction is important for proper allocation and utilization of the available resources. Manipulating energy prices to leverage storage and flexible loads through these demand prediction models is a novel idea that needs to be studied. In this paper, different models for proactive prediction of the energy demand for an entire city using different machine learning techniques are presented and compared. The results of the machine learning techniques show that the proposed nonlinear autoregressive with exogenous inputs neural network model resulted in the most accurate predictions. These prediction models pave the way for the demand side to become an important asset for grid regulation by responding to variable price signals through battery energy storage and passive thermal energy storage using HVAC temperature set points.
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Alshejari, Abeer, Vassilis S. Kodogiannis, and Stavros Leonidis. "Development of Neurofuzzy Architectures for Electricity Price Forecasting." Energies 13, no. 5 (March 5, 2020): 1209. http://dx.doi.org/10.3390/en13051209.
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In 20th century, many countries have liberalized their electricity market. This power markets liberalization has directed generation companies as well as wholesale buyers to undertake a greater intense risk exposure compared to the old centralized framework. In this framework, electricity price prediction has become crucial for any market player in their decision-making process as well as strategic planning. In this study, a prototype asymmetric-based neuro-fuzzy network (AGFINN) architecture has been implemented for short-term electricity prices forecasting for ISO New England market. AGFINN framework has been designed through two different defuzzification schemes. Fuzzy clustering has been explored as an initial step for defining the fuzzy rules while an asymmetric Gaussian membership function has been utilized in the fuzzification part of the model. Results related to the minimum and maximum electricity prices for ISO New England, emphasize the superiority of the proposed model over well-established learning-based models.
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Su, Haokun, Xiangang Peng, Hanyu Liu, Huan Quan, Kaitong Wu, and Zhiwen Chen. "Multi-Step-Ahead Electricity Price Forecasting Based on Temporal Graph Convolutional Network." Mathematics 10, no. 14 (July 6, 2022): 2366. http://dx.doi.org/10.3390/math10142366.
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Traditional electricity price forecasting tends to adopt time-domain forecasting methods based on time series, which fail to make full use of the regional information of the electricity market, and ignore the extra-territorial factors affecting electricity price within the region under cross-regional transmission conditions. In order to improve the accuracy of electricity price forecasting, this paper proposes a novel spatio-temporal prediction model, which is combined with the graph convolutional network (GCN) and the temporal convolutional network (TCN). First, the model automatically extracts the relationships between price areas through the graph construction module. Then, the mix-jump GCN is used to capture the spatial dependence, and the dilated splicing TCN is used to capture the temporal dependence and forecast electricity price for all price areas. The results show that the model outperforms other models in both one-step forecasting and multi-step forecasting, indicating that the model has superior performance in electricity price forecasting.
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Brdyś, Mietek, Adam Borowa, Piotr Idźkowiak, and Marcin Brdyś. "Adaptive Prediction of Stock Exchange Indices by State Space Wavelet Networks." International Journal of Applied Mathematics and Computer Science 19, no. 2 (June 1, 2009): 337–48. http://dx.doi.org/10.2478/v10006-009-0029-z.
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Adaptive Prediction of Stock Exchange Indices by State Space Wavelet NetworksThe paper considers the forecasting of the Warsaw Stock Exchange price index WIG20 by applying a state space wavelet network model of the index price. The approach can be applied to the development of tools for predicting changes of other economic indicators, especially stock exchange indices. The paper presents a general state space wavelet network model and the underlying principles. The model is applied to produce one session ahead and five sessions ahead adaptive predictors of the WIG20 index prices. The predictors are validated based on real data records to produce promising results. The state space wavelet network model may also be used as a forecasting tool for a wide range of economic and non-economic indicators, such as goods and row materials prices, electricity/fuel consumption or currency exchange rates.
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Kontogiannis, Dimitrios, Dimitrios Bargiotas, Aspassia Daskalopulu, Athanasios Ioannis Arvanitidis, and Lefteri H. Tsoukalas. "Error Compensation Enhanced Day-Ahead Electricity Price Forecasting." Energies 15, no. 4 (February 17, 2022): 1466. http://dx.doi.org/10.3390/en15041466.
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The evolution of electricity markets has led to increasingly complex energy trading dynamics and the integration of renewable energy sources as well as the influence of several external market factors contributed towards price volatility. Therefore, day-ahead electricity price forecasting models, typically using some kind of neural network, play a crucial role in the optimal behavior of market agents. The most prominent models and benchmarks rely on improving the accuracy of predictions and the time for convergence by some sort of a priori processing of the dataset that is used for the training of the neural network, such as hyperparameter tuning and feature selection techniques. What has been overlooked so far is the possible benefit of a posteriori processing, which would consider the effects of parameters that could refine the predictions once they have been made. Such a parameter is the estimation of the residual training error. In this study, we investigate the effect of residual training error estimation for the day-ahead price forecasting task and propose an error compensation deep neural network model (ERC–DNN) that focuses on the minimization of prediction error, while reinforcing error stability through the integration of an autoregression module. The experiments on the Nord Pool power market indicated that this approach yields improved error metrics when compared to the baseline deep learning structure in different training scenarios, and the refined predictions for each hourly sequence shared a more stable error profile. The proposed method contributes towards the development of more flexible hybrid neural network models and the potential integration of the error estimation module in future benchmarks, given a small and interpretable set of hyperparameters.
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Marcjasz, Grzegorz, Tomasz Serafin, and Rafał Weron. "Selection of Calibration Windows for Day-Ahead Electricity Price Forecasting." Energies 11, no. 9 (September 7, 2018): 2364. http://dx.doi.org/10.3390/en11092364.
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We conduct an extensive empirical study on the selection of calibration windows for day-ahead electricity price forecasting, which involves six year-long datasets from three major power markets and four autoregressive expert models fitted either to raw or transformed prices. Since the variability of prediction errors across windows of different lengths and across datasets can be substantial, selecting ex-ante one window is risky. Instead, we argue that averaging forecasts across different calibration windows is a robust alternative and introduce a new, well-performing weighting scheme for averaging these forecasts.
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Jan, Faheem, Ismail Shah, and Sajid Ali. "Short-Term Electricity Prices Forecasting Using Functional Time Series Analysis." Energies 15, no. 9 (May 7, 2022): 3423. http://dx.doi.org/10.3390/en15093423.
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In recent years, efficient modeling and forecasting of electricity prices became highly important for all the market participants for developing bidding strategies and making investment decisions. However, as electricity prices exhibit specific features, such as periods of high volatility, seasonal patterns, calendar effects, nonlinearity, etc., their accurate forecasting is challenging. This study proposes a functional forecasting method for the accurate forecasting of electricity prices. A functional autoregressive model of order P is suggested for short-term price forecasting in the electricity markets. The applicability of the model is improved with the help of functional final prediction error (FFPE), through which the model dimensionality and lag structure were selected automatically. An application of the suggested algorithm was evaluated on the Italian electricity market (IPEX). The out-of-sample forecasted results indicate that the proposed method performs relatively better than the nonfunctional forecasting techniques such as autoregressive (AR) and naïve models.
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Anbazhagana, S., and Bhuvaneswari Ramachandran. "Ameliorating Vertically Bundled Electricity Price Prediction Exclusively from ICMLP Network." International Journal of Performability Engineering 17, no. 4 (2021): 364. http://dx.doi.org/10.23940/ijpe.21.04.p4.364370.
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Neupane, Bijay, Wei Woon, and Zeyar Aung. "Ensemble Prediction Model with Expert Selection for Electricity Price Forecasting." Energies 10, no. 1 (January 10, 2017): 77. http://dx.doi.org/10.3390/en10010077.
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Ko, Hee-Sang, Kwang-Y. Lee, and Ho-Chan Kim. "Electricity Price Prediction Model Based on Simultaneous Perturbation Stochastic Approximation." Journal of Electrical Engineering and Technology 3, no. 1 (March 1, 2008): 14–19. http://dx.doi.org/10.5370/jeet.2008.3.1.014.
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Crisostomi, Emanuele, Claudio Gallicchio, Alessio Micheli, Marco Raugi, and Mauro Tucci. "Prediction of the Italian electricity price for smart grid applications." Neurocomputing 170 (December 2015): 286–95. http://dx.doi.org/10.1016/j.neucom.2015.02.089.
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Vilar, Juan, Germán Aneiros, and Paula Raña. "Prediction intervals for electricity demand and price using functional data." International Journal of Electrical Power & Energy Systems 96 (March 2018): 457–72. http://dx.doi.org/10.1016/j.ijepes.2017.10.010.
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Cai, Qinqin, Yongqiang Zhu, Xiaohua Yang, and Lin E. "Alterable Electricity Pricing Mechanism Considering the Deviation of Wind Power Prediction." Sustainability 12, no. 5 (March 1, 2020): 1848. http://dx.doi.org/10.3390/su12051848.
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Fluctuation and prediction errors of wind power would cause a large amount of automatic generation control (AGC) adjustment costs, which lead to the problem of power curtailment. A reasonable mechanism of grid-connection electricity price may encourage wind farms to take measures to reduce the deviation between output power and schedule power, which is helpful for source-network coordination and reducing wind power curtailment. An alterable electricity pricing mechanism considering wind power deviation rate is proposed. In each schedule cycle, electricity price is adjusted according to the deviation rate and its historical change trend. In this way, wind farms will be encouraged to configure energy storage to promote the accordance of wind output power with schedule power to the greatest extent. Given the statistical characteristic of prediction errors of wind power, this paper proposes a schedule power model, taking least squares of output power deviation as objective function, and then puts forward an engineering application method for determining schedule power. This paper analyzes the overall cost and revenue of a wind farm to configure energy storage and determine the optimal energy storage capacity with the goal of maximizing the profit of the wind farm. In the case analysis, the effect of the deviation rate and its historical change trend, the deviation rate tolerance coefficient on electricity price is analyzed. The case analysis demonstrates the effectiveness of the proposed alterable electricity pricing mechanism and shows that the mechanism is helpful at reducing wind power output deviation and wind curtailment.
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Marcjasz, Grzegorz, Bartosz Uniejewski, and Rafał Weron. "Beating the Naïve—Combining LASSO with Naïve Intraday Electricity Price Forecasts." Energies 13, no. 7 (April 3, 2020): 1667. http://dx.doi.org/10.3390/en13071667.
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In the last three decades the vast majority of electricity price forecasting (EPF) research has concerned day-ahead markets. However, the rapid expansion of renewable generation—mostly wind and solar—have shifted the focus to intraday markets, which can be used to balance the deviations between positions taken in the day-ahead market and the actual demand and renewable generation. A recent EPF study claims that the German intraday, continuous-time market for hourly products is weak-form efficient, that is, that the best predictor for the so-called ID3-Price index is the most recent transaction price. Here, we undermine this claim and show that we can beat the naïve forecast by combining it with a prediction of a parameter-rich model estimated using the least absolute shrinkage and selection operator (LASSO). We further argue, that that if augmented with timely predictions of fundamental variables for the coming hours, the LASSO-estimated model itself can significantly outperform the naïve forecast.
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Rokamwar, Kaustubh. "Feed- Forward Neural Network based Day Ahead Nodal Pricing." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 15, 2021): 1029–33. http://dx.doi.org/10.22214/ijraset.2021.36352.
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An electricity locational marginal pricing prediction normally recognized by 24-hour day-ahead nodal price forecast. In this paper first collected all physical and technical data i.e. availability of generation and their cost characteristics, real and reactive demands at various buses, transmission capacity availability at various conditions like peak and off-peak conditions. All these input data are used as input for computation of optimal power flow. The nodal prices are calculated with AC-DC optimal power flow methodology for IEEE 30 bus system. The resulted optimal real electricity bus voltages, nodal prices, reactive and real demands, angles have been given as inputs to Artificial Neural Network (ANN) for predict day ahead nodal prices.
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Zhao, Xin, Qiushuang Li, Wanlei Xue, Yihang Zhao, Huiru Zhao, and Sen Guo. "Research on Ultra-Short-Term Load Forecasting Based on Real-Time Electricity Price and Window-Based XGBoost Model." Energies 15, no. 19 (October 7, 2022): 7367. http://dx.doi.org/10.3390/en15197367.
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With the continuous development of new power systems, the load demand on the user side is becoming more and more diverse and random, which also brings difficulties in the accurate prediction of power load. Although the introduction of deep learning algorithms has improved the prediction accuracy to a certain extent, it also faces problems such as large data requirements and low computing efficiency. An ultra-short-term load forecasting method based on the windowed XGBoost model is proposed, which not only reduces the complexity of the model, but also helps the model to capture the autocorrelation effect of the forecast object. At the same time, the real-time electricity price is introduced into the model to improve its forecast accuracy. By simulating the load data of Singapore’s electricity market, it is proved that the proposed model has fewer errors than other deep learning algorithms, and the introduction of the real-time electricity price helps to improve the prediction accuracy of the model. Furthermore, the broad applicability of the proposed method is verified by a sensitivity analysis on data with different sample sizes.
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avi, R. Rag, M. S. Kam alesh, and N. Senthil nathan. "Day Ahead Electricity Price Prediction for a Distribution System in India." International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering 04, no. 02 (February 20, 2015): 669–78. http://dx.doi.org/10.15662/ijareeie.2015.0402024.
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Kim, Chang-il, In-Keun Yu, and Y. H. Song. "Prediction of system marginal price of electricity using wavelet transform analysis." Energy Conversion and Management 43, no. 14 (September 2002): 1839–51. http://dx.doi.org/10.1016/s0196-8904(01)00127-3.
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Chaâbane, Najeh. "A hybrid ARFIMA and neural network model for electricity price prediction." International Journal of Electrical Power & Energy Systems 55 (February 2014): 187–94. http://dx.doi.org/10.1016/j.ijepes.2013.09.004.
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Biber, Albert, Mine Tunçinan, Christoph Wieland, and Hartmut Spliethoff. "Negative price spiral caused by renewables? Electricity price prediction on the German market for 2030." Electricity Journal 35, no. 8 (October 2022): 107188. http://dx.doi.org/10.1016/j.tej.2022.107188.
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Daniel, Gil-Vera Victor. "Smart Grid Stability Prediction with Machine Learning." WSEAS TRANSACTIONS ON POWER SYSTEMS 17 (October 6, 2022): 297–305. http://dx.doi.org/10.37394/232016.2022.17.30.
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Smart grids refer to a grid system for electricity transmission, which allows the efficient use of electricity without affecting the environment. The stability estimation of this type of network is very important since the whole process is time-dependent. This paper aimed to identify the optimal machine learning technique to predict the stability of these networks. A free database of 60,000 observations with information from consumers and producers on 12 predictive characteristics (Reaction times, Power balances, and Price-Gamma elasticity coefficients) and an independent variable (Stable / Unstable) was used. This paper concludes that the Random Forests technique obtained the best performance, this information can help smart grid managers to make more accurate predictions so that they can implement strategies in time and avoid collapse or disruption of power supply.
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Wu, Kehe, Yanyu Chai, Xiaoliang Zhang, and Xun Zhao. "Research on Power Price Forecasting Based on PSO-XGBoost." Electronics 11, no. 22 (November 16, 2022): 3763. http://dx.doi.org/10.3390/electronics11223763.
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With the reform of the power system, the prediction of power market pricing has become one of the key problems that needs to be solved in time. Power price prediction plays an important role in maximizing the profits of the participants in the power market and making full use of power energy. In order to improve the prediction accuracy of the power price, this paper proposes a power price prediction method based on PSO optimization of the XGBoost model, which optimizes eight main parameters of the XGBoost model through particle swarm optimization to improve the prediction accuracy of the XGBoost model. Using the electricity price data of Australia from January to December 2019, the proposed model is compared with the XGBoost model. The experimental results show that PSO can effectively improve the performance of the model. In addition, the prediction results of PSO-XGBoost are compared with those of SVM, LSTM, ARIMA, RW and XGBoost, and the average relative error and root mean square error of different power price prediction models are calculated. The experimental results show that the prediction accuracy of the PSO-XGBoost model is higher and more in line with the actual trend of power price change.