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Day-ahead electricity price forecasting (DAEPF) holds critical significance for stakeholders in energy markets, particularly in areas with large amounts of renewable energy sources (RES) integration. In Japan, the proliferation of RES has led to instances wherein day-ahead electricity prices drop to nearly zero JPY/kWh during peak RES production periods, substantially affecting transactions between electricity retailers and consumers. This paper introduces an innovative DAEPF framework employing a Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) model designed to predict day-ahead electricity prices in the Kyushu area of Japan. To mitigate the inherent uncertainties associated with neural networks, a novel ensemble learning approach is implemented to bolster the DAEPF model's robustness and prediction accuracy. The CNN–LSTM model is verified to outperform a standalone LSTM model in both prediction accuracy and computation time. Additionally, applying a natural logarithm transformation to the target day-ahead electricity price as a pre-processing technique has proven necessary for higher prediction accuracy. A novel "policy-versus-policy" strategy is proposed to address the prediction problem of the zero prices, halving the computation time of the traditional two-stage method. The efficacy of incorporating a suite of multimodal features: areal day-ahead electricity price, day-ahead system electricity price, areal actual power generation, areal meteorological forecasts, calendar forecasts, alongside the rolling features of areal day-ahead electricity price, as explanatory variables to significantly enhance DAEPF accuracy has been validated. With the full integration of the proposed features, the CNN–LSTM ensemble model achieves its highest accuracy, reaching performance metrics of R 2 , MAE, and RMSE of 0.787, 1.936 JPY/kWh, and 2.630 JPY/kWh, respectively, during the test range from 1 March 2023 to 31 March 2023, underscoring the advantages of a comprehensive, multi-dimensional approach to DAEPF. [ABSTRACT FROM AUTHOR]

In competitive electricity markets, prices are determined by the collective behaviour of suppliers and consumers. Hence, these systems rely on the balance between supply and demand, and sudden changes in the underlying conditions can lead to significant price fluctuations. In the face of the recent transformations in Italian electricity markets, which are driven by an increasing share of non-programmable renewables, energy crises, and geopolitical tensions, our study focuses on effective forecasting methodologies. We compare kernel and linear regression for predicting market equilibrium prices, both in point and probabilistic sense. We showcase the potential of both linear and non-linear models when carefully engineering the problem of interest, which involves properly selecting data and variables. The noteworthy outcome is that, while linear and non-linear models may differ in nature, their performance converges closely, attesting to the robustness of our approach in achieving reliable forecasts. We describe data sources and assumptions in exploratory univariate analyses, and the performance of the final multivariate model is evaluated over a test period on September 2023.

Power load forecasting in Data Analytics is an emerging technology. In this paper, we have proposed the Generative Adversarial Networks (GANS) neural network model as the classifier for probabilistic electricity price forecasting. To assess the performance of these frameworks, we apply our models on the dataset cater by (IESO) in Ontario, Canada. We have compared our proposed model with Random Forest, Support vector machine (SVM), and XG-Boost. MSE, RMSE, MAE metrices are considered for the evaluation of the model's performance. The outcome indicates that the mean squared error (MSE) of our proposed model is 687.513 whereas the MSE of existing methodologies is 830.15, 746.812, and 776.201 which is more than our proposed methodology. Mean absolute error (MAE) of SVM and our proposed GANS Neural Network (EPFEG) have the lowest MAE that is 8%. Furthermore, EPFEG achieved almost 7% better accuracy than existing schemes. [ABSTRACT FROM AUTHOR]

This paper describes the development of a deep neural network architecture based on transformer encoder blocks and Time2Vec layers for the prediction of electricity prices several steps ahead (8 h), from a probabilistic approach, to feed future decision-making tools in the context of the widespread use of intra-day DERs and new market perspectives. The proposed model was tested with hourly wholesale electricity price data from Colombia, and the results were compared with different state-of-the-art forecasting baseline-tuned models such as Holt–Winters, XGBoost, Stacked LSTM, and Attention-LSTM. The findings show that the proposed model outperforms these baselines by effectively incorporating nonlinearity and explicitly modeling the underlying data's behavior, all of this under four operating scenarios and different performance metrics. This allows it to handle high-, medium-, and low-variability scenarios while maintaining the accuracy and reliability of its predictions. The proposed framework shows potential for significantly improving the accuracy of electricity price forecasts, which can have significant benefits for making informed decisions in the energy sector. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Electricity price forecasts have become a fundamental factor affecting the decision-making of all market participants. Extreme price volatility has forced market participants to hedge against volume risks and price movements. Hence, getting an accurate price forecast from a few hours to a few days ahead is very important and very challenging due to various factors. This paper proposes an integrated long-term recurrent convolutional network (ILRCN) model to predict electricity prices considering the majority of contributing attributes to the market price as input. The proposed ILRCN model combines the functionalities of a convolutional neural network and long short-term memory (LSTM) algorithm along with the proposed novel conditional error correction term. The combined ILRCN model can identify the linear and nonlinear behavior within the input data. ERCOT wholesale market price data along with load profile, temperature, and other factors for the Houston region have been used to illustrate the proposed model. The performance of the proposed ILRCN electricity price forecasting model is verified using performance/evaluation metrics like mean absolute error and accuracy. Case studies reveal that the proposed ILRCN model shows the highest accuracy and efficiency in electricity price forecasting as compared to the support vector machine (SVM) model, fully connected neural network model, LSTM model, and the traditional LRCN model without the conditional error correction stage. [ABSTRACT FROM AUTHOR]

The Iberian Market for Electricity resulted from a cooperation process developed by the Portuguese and Spanish administrations, aiming to promote the integration of the electrical systems of both countries. This common market consists of organised markets or power exchanges, and non-organised markets where bilateral over-the-counter trading takes place with or without brokers. Within this scenario, electricity price forecasts have become fundamental to the process of decision-making and strategy development by market participants. The unique characteristics of electricity prices such as non-stationarity, non-linearity and high volatility make this task very difficult. For this reason, instead of a simple time forecast, market participants are more interested in a causal forecast that is essential to estimate the uncertainty involved in the price. This work focuses on modelling the impact of various explanatory variables on the electricity price through a multiple linear regression analysis. The quality of the estimated models obtained validates the use of statistical or causal methods, such as the Multiple Linear Regression Model, as a plausible strategy to achieve causal forecasts of electricity prices in medium and long-term electricity price forecasting. From the evaluation of the electricity price forecasting for Portugal and Spain, in the year of 2017, the mean absolute percentage errors (MAPE) were 9.02% and 12.02%, respectively. In 2018, the MAPE, evaluated for 9 months, for Portugal and Spain equals 7.12% and 6.45%, respectively.

Accurate short-term price forecasting is crucial for the power system and electricity market. This paper proposes a hybrid short-term electricity price forecasting model based on empirical mode decomposition (EMD) and deep neural network (DNN). Firstly, EMD is used to denoise the data set. Next, the reconstructed data are input into the DNN composed of a convolutional neural network (CNN) and long-short-term memory (LSTM) neural network to analyze the characteristics and output the prediction results. Finally, the superiority of this model is verified by comparing the electricity price data of the Australian electricity market with a single LSTM network, EMD-CNN model, and CNN-LSTM model. [ABSTRACT FROM AUTHOR]

Abstact: In the deregulated electricity market, it is increasingly important to accurately predict the fluctuating, nonlinear, and high‐frequent electricity price for market decision‐making. However, the uncertainties associated with electricity prices, such as non‐stationarity, nonlinearity, and high volatility, pose critical difficulties for electricity price forecasting (EPF). Unlike point forecasting, which provides only a single, deterministic estimate of future prices, probabilistic forecasting gives a more comprehensive and nuanced picture of future price dynamics, which can help market participants make better‐informed decisions when facing uncertainty. Therefore, in this paper, we propose a robust deep learning method for multi‐step probabilistic forecasting. First, we use the least absolute shrinkage and selection operator (LASSO) in the expert model to generate point forecasts. Second, we introduce the smoothly clipped absolute deviation regularization term, a nonconvex penalty with proven oracle properties in model selection, into temporal fusion transformers. Finally, we employ the proposed model to integrate point forecasts to give probabilistic forecasts. To evaluate the proposed forecasting model, real‐data experiments are conducted in the Nord Pool electricity market and the Polish Power Exchange market. Empirical results show that the proposed model has demonstrated superior probabilistic forecasting performances compared with other competitors and has proven its effectiveness in real‐world applications. [ABSTRACT FROM AUTHOR]

Electricity Price Forecasting (EPF) influences the sale conditions in the energy sector. Proper models of electricity price prognosis can be decisive for choice between energy sources as a start point of transformation toward renewable energy sources. This article aims to present and compare various EPF models scientific publications. Adopted in this study procedure, the EPF publications models are compared into two main categories: the most popular and the most accurate. The adopted method is a bibliometric study as a variation of Systematic Literature Review (SLR) with specified automated queries supported by the VOSviewer bibliometric maps exploration. The subject of this research is the exploration of EPF models in two databases, Web of Science and Scopus, and their content comparison. As a result, the SLR research queries were classified into two groups, the most cited and most accurate models. Queries characteristics were explained, along with the graphical presentation of the results. Future promising research avenues can be dedicated to the most accurate EPF model formulation proved by statistical testing of its significance and accuracy. [ABSTRACT FROM AUTHOR]

Short-term electricity price forecasting in deregulated electricity markets has been studied extensively in recent years but without significant reduction in price forecasting errors. Also demand-side management and short-term scheduling operations in smart grids do not require strictly very accurate forecast and can be executed with certain practical price thresholds. This paper, therefore, presents a multikernel extreme learning machine (MKELM) for both short-term electricity price forecasting and classification according to some prespecified price thresholds. The kernel ELM does not require the hidden layer mapping function to be known and produces robust prediction and classification in comparison with the conventional ELM using random weights between the input and hidden layers. Further in the MKELM formulation, the linear combination of the weighted kernels is optimized using vaporization precipitation-based water cycle algorithm (WCA) to produce significantly accurate electricity price prediction and classification. The combination of MKELM and WCA is named as WCA-MKELM in this work. To validate the effectiveness of the proposed approach, three electricity markets, namely PJM, Ontario and New South Wales, are considered for electricity price forecasting and classification producing fairly accurate results. [ABSTRACT FROM AUTHOR]

Penetration of renewable energy resources into grid is necessary to meet the elevated demand of electricity. In view of this penetration of solar and wind power increasing immensely across the globe. Solar energy is widely expanding in terms of generation and capacity addition due its better predictability over wind energy. Electricity pricing is one of the important aspects for power system planning and it felicitates information for the electricity bidder for accurate electricity generation and resource allocation. The important task is to forecast the electricity price accurately in grid interactive environment. This task is tedious in renewable integrated market due to intermittency issue. In this paper, investigation has been done on the effect of solar energy generation on electricity price forecasting. Different state of the art Machine learning (ML) models have been applied and compared with LSTM model for electricity price forecasting and the evaluation of the impact of solar energy generation on electricity price has been done. During the investigation it was found from the results that the LSTM model outperform all other models and impact of solar energy generation on electricity price is evaluated using forecasting metrics. The forecasted electricity price considering the factor of solar energy generation was lower as compared with the forecast without solar energy generation. The reliability test of the MAPE values has been performed by calculating confidence interval for proposed model. [ABSTRACT FROM AUTHOR]

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This paper presents a novel hybrid model integrating maximal overlap discrete wavelet transform (MODWT) denoising and empirical mode decomposition (EMD) with sequence-to-sequence (seq2seq) long short-term memory (LSTM) neural networks for day-ahead electricity price forecasting. The nonstationary and nonlinear time series electricity price data are first denoised using MODWT. The resulting signal is decomposed into several intrinsic mode functions (IMF) with different resolutions by EMD. The extracted IMF is then introduced into seq2seq LSTM to obtain an aggregated predicted value for electricity price. The proposed method is examined using the Nord pool Elspot energy market data. Empirical results show that the proposed model outperformed the other forecasting models like LSTM and stacked LSTM. The performance measures indicate that data denoising can significantly improve the prediction stability and the generalization ability of the LSTM model. [ABSTRACT FROM AUTHOR]

In electricity price forecast, deep networks have shown promising due to their strong nonlinear modeling capability but corresponding design tricks are not convincible, reflecting the situation that biologically-inspired networks in time-series analysis are still not enough explored. To this end, this paper proposes a m ulti- s cale v isual-inspired network with three explored modules, namely MSV-Net, for electricity price forecasting. In particular, through simulating both the ocular globe and neurons in signal conversion and further enhancement, the multi-scale feature extraction module captures the overall and specific time-scale features via multiple convolutional kernels and especially highlights the core features via self-attention mechanisms. Through imitating the left and right hemispheres in analyzing and producing complementary visual information, the dual-stream collaboration module adopts the convolutional layer and gated recurrent unit to extract and integrate time-dependent relationships in parallel, thus improving the model stability. Through simulating the higher visual region in understanding complex environments, the forecasting module is introduced to forecast the electricity price. The layer-by-layer collaboration of the above three modules guarantees the effectiveness of the MSV-Net. Moreover, four comprehensive evaluation metrics are explored to assess the forecasting performance in peak, low, and normal samples. Experiments and discussions under two electricity price datasets show that the MSV-Net has satisfactory forecasting accuracy and stability, in improving average mean absolute percent error of 31.59 % and 25.15 % for two datasets compared with 12 baselines, and shows extensible in other energy forecasting fields. • Short-term electricity price series involves multi-time-scale features. • A multi-scale visual-inspired network (MSV-Net) is explored. • Intelligent feature extraction ensures the effectiveness of MSV-Net. • Four novel comprehensive evaluation metrics are proposed. • Its high performance is reflected via four experiments and six discussions. [ABSTRACT FROM AUTHOR]

Summary: A Ubiquitous Power Internet of Things is fundamentally an Internet of Things, but focused upon power systems. Being able to predict these prices accurately may help with the identification of customer needs and the effective regulation of the power grid by power producers. It may also help electric power traders to manage risks, make correct decisions, and obtain more benefits. In this paper, a novel hybrid model is proposed for short‐term electricity price prediction. The model consists of three algorithms: Variational Mode Decomposition (VMD); a Convolutional Neural Network (CNN); and Gated Recurrent Unit (GRU). This is called SEPNet for convenience. The annual electricity price data is divided into seasons because of seasonal differences in the time series of electricity prices. The VMD algorithm is used to decompose the complex time series of electricity prices into intrinsic mode functions (IMFs) with different center frequencies. The CNN is used to further extract the time‐domain features for all the intrinsic model functions in the VMD domain. The GRU is then employed to process and learn the time‐domain features extracted by the CNN, leading to the final prediction. A comparison is made with five models, such as LSTM, CNN, VMD‐CNN, BP, VMD‐ELMAN. The results showed that the proposed model had the best performance, and it was found that using VMD can improve the Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) for the four seasons by 84% and 81%, respectively. The addition of GRU in the SEPNet model further improved the MAPE and RMSE by 19% and 25%, respectively. Including CNN and VMD‐CNN, that shows that the proposed model has the best performance. The MAPE and RMSE for the four seasonal averages are 0.730% and 0.453, respectively. This confirms that the SEPNet model has the feasibility and high accuracy to predict short‐term electricity prices. [ABSTRACT FROM AUTHOR]

Electricity price forecasting (EPF) has been challenged by the widespread grid integration of renewable energy (RE), so it is critical to develop a highly accurate and reliable EPF model. In this study, novel considerations of RE generation factors are made, and a quantitative model for the impact of RE on electricity prices is built using random forests (RF) and improved Mahalanobis Distance (IMD). To reduce data duplication, similar days of EPF are first selected. Then it is suggested to decompose the electricity price series into multiple intrinsic mode functions (IMF) and residuals with different frequencies using a two-layer decomposition model based on improved comprehensive ensemble empirical mode decomposition (ICEEMD) and variational mode decomposition (VMD), in order to reduce data noise and volatility. Finally, EPF model based on Bi-directional long short-term memory (Bi-LSTM) is established to forecast multiple subsequences, and the final price forecasting result is obtained after integrated processing. Experimental results show that the RF-IMD-ICEEMD-VMD-Bi-LSTM hybrid model can significantly improve the forecasting performance, reduce the prediction error of EPF, and has the best performance among the comparison models. • A similar day screening model for electricity price forecasting based on random forests and improved Mahalanobis distance is constructed. • The two-layer decomposition technology based on comprehensive ensemble empirical mode decomposition and variational mode decomposition is established to eliminate data noise. • Bi-LSTM is used to predict the sub-sequences obtained by ICEEMD-VMD two-layer decomposition. [ABSTRACT FROM AUTHOR]

Highlights • A data preprocessing approach named the dual decomposition strategy is developed. • A novel forecasting system is developed for electricity price forecasting. • The accuracy and stability of the forecasting system are improved simultaneously. • The applicability and effectiveness are verified in a real electricity market. Abstract Electricity price forecasting plays a crucial role in balancing electricity generation and consumption, which is of great political and economic significance for all of society but is still a challenging task. However, in previous studies, most researchers have focused on improving either forecasting accuracy or stability while ignoring the significance of performing these tasks simultaneously. More importantly, few researchers have deeply studied the data preprocessing strategy, only focusing on the application of individual decomposition approaches. Therefore, a novel hybrid forecasting system based on a dual decomposition strategy and multi-objective optimization is developed for electricity price forecasting that includes four modules: a data preprocessing module, optimization module, forecasting module and evaluation module. In this system, an effective multi-objective optimization algorithm is employed to guarantee simultaneous improvements in accuracy and stability. In addition, an improved data preprocessing approach named the dual decomposition strategy is developed, which successfully overcomes the potential drawback of the individual decomposition approach and further improves the effectiveness of the developed forecasting system. Moreover, the evaluation module is incorporated to verify the superiority of the developed forecasting system. Case studies utilizing half-hourly electricity price data collected from New South Wales, Australia are employed as examples. The results prove the superiority of the multi-objective optimization algorithm and the developed dual decomposition strategy and reveal that the developed forecasting system outperforms all of the considered comparison models, which shows its better ability to forecast future electricity prices with better accuracy and stability. [ABSTRACT FROM AUTHOR]

Electricity price forecasting is very important for market participants in a deregulated market. However, only a few papers investigated the impact of forecasting errors on the market participants' behaviours and revenues. In this study, a general formulation of bidding in the electricity market is considered and the participant is assumed to be a price-taker which is general for most of the participants in power markets. A numerical method for quantifying the impact of forecasting errors on the bidding curves and revenues based on multiparametric linear programming is proposed. The forecasted prices are regarded as exogenous parameters for both deterministic and stochastic bidding models. Compared with the existing method, the proposed method can calculate how much improvement will be achieved in the cost or revenue of the bidder if he reduces the price forecasting error level, and such calculation does not require any predefined forecasting results. Numerical results and discussions based on real-market price data are conducted to show the application of the proposed method. [ABSTRACT FROM AUTHOR]

Bidding competition is one of the main transaction approaches in a deregulated electricity market. Locational marginal prices (LMPs) resulting from bidding competition and system operation conditions indicate electricity values at a node or in an area. The LMP reveals important information for market participants in developing their bidding strategies. Moreover, LMP is also a vital indicator for the Security Coordinator to perform market redispatch for congestion management. This paper presents a method using a principal component analysis (PCA) network cascaded with a multi-layer feedforward (MLF) network for forecasting LMPs in a day-ahead market. The PCA network extracts essential features from periodic information in the market. These features serve as inputs to the MLF network for forecasting LMPs. The historical LMPs in the PJM market are employed to test the proposed method. It is found that the proposed method is capable of forecasting day-ahead LMP values efficiently.

Short-term electricity price forecasting models are typically estimated via rolling windows, i.e. by using only the most recent observations. Nonetheless, the literature does not provide guidelines on how to select the optimal size of such windows. This paper shows that determining the appropriate window prior to estimation dramatically improves forecasting performances. In addition, it proposes a simple two-step approach to choose the best performing models and window sizes. The value of this methodology is illustrated by analyzing hourly datasets from two large power markets (Nord Pool and IPEX) with a selection of eleven different forecasting models. Incidentally, our empirical application reveals that simple models, such as a simple linear regression (SLR) with only two parameters, can perform unexpectedly well if estimated on extremely short samples. Surprisingly, in the Nord Pool, such SLR is the best performing model in 13 out 24 trading periods. [ABSTRACT FROM AUTHOR]

In this paper an efficient method is proposed for electricity price forecasting. This paper focuses on Locational Marginal Price (LMP) that efficiently maintains power markets by alleviating transmission network congestion. There are complicated behaviors of the time series due to uncertain factors in the power markets. From a standpoint of market players, a sophisticated method is required to forecast LMP effectively. The proposed method makes use of the hybridization of GP (Gaussian Process) of hierarchical Bayesian estimation, EPSO (Evolutionary Particle Swarm Optimization) of evolutionary computation and fuzzy c-means of allowing data to belong to two or more clusters. EPSO is used to improve the accuracy of parameters in MAP (Maximum a Posteriori) estimation for GP. The use of fuzzy c-mean is useful for increasing the number of learning data for GP to deal with spikes. The effectiveness of the proposed method is demonstrated for real LMP data. [ABSTRACT FROM AUTHOR]

Reliable electricity price forecasts are of great importance to operators and participants in power markets. However, due to mixing effects of various factors, electricity price fluctuations are too complex to extract hidden features for accurately modelling. Additionally, biologically-inspired ideas are promising in significantly improving the performance and rationality of deep forecasting networks. Based on the fact that the auditory system effectively handles complex sound signals with amplitude, frequency and wavelength characteristics, this paper explores an auditory-circuit-motivated deep network with three following modules for forecasting short-term electricity prices. Specifically, the coding module imitates that left and right ears receive the sound and convert it into electric signals in parallel, and codes the input electricity prices into multiple valuable features, improving the feature integrity and model stability. The analysis module imitates that the left and right hemispheres integrate and handle electric signals to produce higher auditory information, and captures nonlinear and short-term dependencies via stacking convolutional and gating operations. The forecasting module imitates that the high-level brain region pays attention to the external environment based on higher auditory information, and generates final forecasts via the attention mechanism. Based on the cooperation of the above three modules, the proposed deep network mimics the flowchart, structures and functions, thereby inheriting superior handling capability of the auditory circuit. Experiment results under two real-world sets show that the proposed deep network is superior to 13 baselines, and improves the mean absolute error by averages of 14.7 % and 20.6 % in normal parts, 12.9 % and 23.4 % in high-fluctuation parts, and 16.1 % and 16.2 % in peak-low parts, which is an effective supplementary model for electricity price forecasting. • An auditory-circuit-motivated deep network is developed. • Forecasting applications are performed for short-term electricity prices. • Flowchart, structures, and functions of the auditory circuit are imitated. • Gating and attention mechanisms improve the forecasting accuracy. • Six experiments show high performance of the bionic deep network. [ABSTRACT FROM AUTHOR]

• A novel deep learning-based framework with correlation analysis and parameter optimization is proposed and applied to model day-ahead electricity price scenarios. • The proposed hybrid prediction algorithm named convolutional neural network+stacked sparse denoising auto-encoders has the ability to improve prediction accuracy and stability of results significantly. • The network architecture of convolutional neural network+stacked sparse denoising auto-encoders can accelerate training time and convergence. • ICEEMDAN is introduced into the hybrid prediction framework, which can enhance the performance of hybrid prediction model. Day-ahead electricity price forecasting plays a vital role in electricity markets under liberalization and deregulation, which can provide references for participants in bidding strategies, energy trading, and risk management. However, due to various uncertain factors, electricity prices often exhibit nonlinearity, randomness, and volatility, adding technical difficulties to accurate price forecasting. To address these difficulties, A novel hybrid deep learning-based model named convolutional neural network+stacked sparse denoising auto-encoders is proposed first. Moreover, the improved complete ensemble empirical mode decomposition with adaptive noise, a decomposition method, is introduced to enhance model performance by the decomposition of complex data sequences. Each intrinsic mode function sub-component obtained by decomposition is separately predicted using the proposed hybrid model, and the forecast result of day-ahead prices is superimposed finally. Taking the Australian national electricity market as a case study, the experimental results verify that the proposed hybrid model can effectively improve prediction accuracy and stability, and shows outstanding prediction performance for price spikes. Furthermore, the proposed model can save training time for neural networks in the prediction process thanks to its faster convergence speed. Hence, the proposed deep learning-based hybrid predictive model can provide a technology-based reference for electricity market participants. [Display omitted] [ABSTRACT FROM AUTHOR]

One of the most significant issues in all nations is the price of electricity. Quantile Regression and the Kalman Filter method are used in this study to forecast the price of electricity in the pay-as-bid market, which is used in many nations, including Iran, Italy, Germany, the UK and many more. The results of our study suggest that Quantile Regression is the most accurate model, particularly when 20th compared to 90th quantiles and the Kalman Filter method. Moreover, geographical and climate considerations play a significant role in setting electricity pricing. Also, it is important to consider the positive correlations between the electricity price and temperature, as well as its negative and uncertain relationship with other variables, including wind speed and humidity. • Quantile regression was more accurate than Kalman Filter method. • Positive and negative relationship between electricity price and demand side variables. • Geographical and climate conditions play significant roles in electricity price. [ABSTRACT FROM AUTHOR]

The predispatch price forecast plays a key element in the electricity market. However, such a forecast usually depends on the traditional offline batch-learning technologies, which cannot respond in time to the unexpected changes in the local power system environment. Further, the predispatch local price forecast is often affected by the dynamic price changes from the neighboring regions. This article proposes a novel online learning forecast approach to overcome the above issues to provide a better predispatch price forecast by using the online sequential extreme learning machine (OS-ELM) algorithm. The article proposes a novel data structure in the form of a 2-D orthogonal list and two corresponding OS-ELM modules. One module provides the rolling day-ahead price prediction and prediction intervals using the day-by-day online training update, while the other provides the rolling 30-min prediction using the 2-h-by-2-h online training update. The proposed approach can continuously perceive any unexpected events and any price fluctuations from the neighboring regions in the nonlinear patterns. The proposed approach is validated using simulation studies based on the data from the Australian electricity market, and the simulation results show that the proposed approach can help in improving the forecast accuracy, especially when unexpected changes occur both locally and in the neighboring area. [ABSTRACT FROM AUTHOR]

A model for forecasting the electricity price in Colombia using artificial neural networks is proposed in this work. Two neural networks structures including the price series in the first and the price series plus the water reserve levels in the latter are used. The results are compared with a Generalized Autorregresive Conditional Heteroskedastic Model (GARCH) model, which shows better adjustment inside the training period, but the neural networks have better performance forecasting outside the training sample. Historical data was supplied by the Company XM belonging to ISA Group, where 120 days were used as training patterns and the next 31 days were left to test the next month forecast.

This paper proposes a new hybrid model to forecast electricity market prices up to four days ahead. The components of the proposed model are combined in two dimensions. First, on the "vertical" dimension, long short-term memory (LSTM) neural networks and extreme gradient boosting (XGBoost) models are stacked up to produce supplementary price forecasts. The final forecasts are then picked depending on how the predictions compare to a price spike threshold. On the "horizontal" dimension, five models are designed to extend the forecasting horizon to four days. This is an important requirement to make forecasts useful for market participants who trade energy and ancillary services multiple days ahead. The horizontally cascaded models take advantage of the availability of specific public data for each forecasting horizon. To enhance the forecasting capability of the model in dealing with price spikes, we deploy a previously unexplored input in the proposed methodology. That is, to use the recent variations in the output power of thermal units as an indicator of unplanned outages or shift in the supply stack. The proposed method is tested using data from Alberta's electricity market, which is known for its volatility and price spikes. An economic application of the developed forecasting model is also carried out to demonstrate how several market players in the Alberta electricity market can benefit from the proposed multi-day ahead price forecasting model. The numerical results demonstrate that the proposed methodology is effective in enhancing forecasting accuracy and price spike detection. [ABSTRACT FROM AUTHOR]

The Iberian Market for Electricity resulted from a cooperation process developed by the Portuguese and Spanish administrations, aiming to promote the integration of the electrical systems of both countries. This common market consists of organised markets or power exchanges, and non-organised markets where bilateral over-the-counter trading takes place with or without brokers. Within this scenario, electricity price forecasts have become fundamental to the process of decision-making and strategy development by market participants. The unique characteristics of electricity prices such as non-stationarity, non-linearity and high volatility make this task very difficult. For this reason, instead of a simple time forecast, market participants are more interested in a causal forecast that is essential to estimate the uncertainty involved in the price. This work focuses on modelling the impact of various explanatory variables on the electricity price through a multiple linear regression analysis. The quality of the estimated models obtained validates the use of statistical or causal methods, such as the Multiple Linear Regression Model, as a plausible strategy to achieve causal forecasts of electricity prices in medium and long-term electricity price forecasting. From the evaluation of the electricity price forecasting for Portugal and Spain, in the year of 2017, the mean absolute percentage errors (MAPE) were 9.02% and 12.02%, respectively. In 2018, the MAPE, evaluated for 9 months, for Portugal and Spain equals 7.12% and 6.45%, respectively. [ABSTRACT FROM AUTHOR]

A functional time series is the realization of a stochastic process where each observation is a continuous function defined on a finite interval. These processes are commonly found in electricity markets and are gaining more importance as more market data become available and markets head toward continuous-time marginal pricing approaches. Forecasting these time series requires models that operate with continuous functions. This paper proposes a new functional forecasting method that attempts to generalize the standard seasonal ARMAX time series model to the $L^2$ Hilbert space. The structure of the proposed model is a linear regression where functional parameters operate on functional variables. The variables can be lagged values of the series (autoregressive terms), past observed innovations (moving average terms), or exogenous variables. In this approach, the functional parameters used are integral operators whose kernels are modeled as linear combinations of sigmoid functions. The parameters of each sigmoid are optimized using a Quasi-Newton algorithm that minimizes the sum of squared errors. This novel approach allows us to estimate the moving average terms in functional time series models. The new model is tested by forecasting the daily price profile of the Spanish and German electricity markets and it is compared to other functional reference models. [ABSTRACT FROM PUBLISHER]

Energy cost-aware scheduling, i.e., scheduling that adapts to real-time energy price volatility, can save large energy consumers millions of dollars every year in electricity costs. Energy price forecasting coupled with energy price-aware scheduling, is a step toward this goal. In this work, we study cost-aware schedules and the effect of various price forecasting schemes on the end schedule-cost. We show that simply optimizing price forecasts based on classical regression error metrics (e.g., Mean Squared Error), does not work well for scheduling. Price forecasts that do result in significantly better schedules, optimize a combination of metrics, each having a different impact on the end-schedule-cost. For example, both price estimation and price ranking are important for scheduling, but they carry different weight. We consider day-ahead energy price forecasting using the Irish Single Electricity Market as a case-study, and test our price forecasts for two real-world scheduling applications: animal feed manufacturing and home energy management systems. We show that price forecasts that co-optimize price estimation and price ranking, result in significant energy-cost savings. We believe our results are relevant for many real-life scheduling applications that are currently plagued with very large energy bills. [ABSTRACT FROM AUTHOR]

Electricity price forecasting (EPF) plays an indispensable role in the decision-making processes of electricity market participants. However, the complexity of electricity markets has made EPF increasingly difficult. Currently, popular methods for EPF are based on signal decomposition and suffer from computational redundancy and hyperparameter optimization challenges. In this paper, we propose a new hybrid forecasting framework to improve the forecasting accuracy of day-ahead electricity prices. The proposed model consists of three valuable strategies. First, an adaptive copula-based feature selection (ACBFS) algorithm based on the maximum correlation minimum redundancy criterion is proposed for selecting model input features. Second, a new method of signal decomposition technique for EPF field is proposed based on decomposition denoising strategy. Third, a Bayesian optimization and hyperband (BOHB) optimized long short-term memory (LSTM) model is used to improve the effect of hyperparameter settings on the prediction results. The effectiveness of the different techniques was broadly cross-validated using five datasets set up for the PJM electricity market, and the results indicated that the proposed hybrid algorithm is more effective and practical for day-ahead EPF. • A novel electricity price forecasting model is proposed. • An adaptive feature selection algorithm is proposed to optimize the input features. • A novel method of combining VMD with time series forecasting is proposed. • BOHB is used to optimize the LSTM hyperparameters. [ABSTRACT FROM AUTHOR]

Given that the price signal in the electricity market is highly volatile or otherwise uncertain, short-term forecasting is significantly affected. Since time-series methods cannot estimate such nonlinear models appropriately with high accuracy, we need to provide an efficient model. For this reason, in this paper, a new hybrid algorithm for day-ahead electricity price forecasting is proposed. In order to achieve this model, we first divide the forecasting problem into three main layers: preprocessor, training, and regulator. In the first layer, we use the curvelet transform to reduce possible noise in the price signal. Then, using the extended data selection model based on increasing correlation and decreasing redundancy, we eliminate the unnecessary data and reduce the volume of computation significantly. Then the regularized data is entered into the learning layer which is a developed Extreme Learning Machine (ELM) to obtain and extract the best pattern from the input data. Since adjusting the control parameters of the proposed ELM can maximize its ability to derive a nonlinear pattern from the price signal, a new developed Virus Colony Search (VCS) method based on the time-varying coefficients theory is proposed in the last layer. The proposed algorithm is a novel optimization method based on the function of viruses to destroy host cells and penetrate the best ones into a cell for replication. The proposed method is applied to existing real electricity markets and the results are compared based on prediction error rates and error-based criteria. The obtained results show the appropriate and acceptable performance of the proposed forecasting method. [ABSTRACT FROM AUTHOR]

SUMMARY: In this paper, a hybrid model of fuzzy clustering and ANN (Artificial Neural Network) is proposed for electricity price forecasting. Due to the complicated behavior of electricity price in power markets, market players are interested in maximizing profits while minimizing risks. As a result, more accurate models are required to deal with electricity price forecasting. This paper proposes a new method that makes use of fuzzy clustering preconditioned GRBFN (Generalized Radial Basis Function Network) to provide more accurate predicted prices. Fuzzy clustering plays a key role to prevent the number of learning data from decreasing at each cluster. GRBFN is one of efficient ANNs to approximate nonlinear systems. Furthermore, a modified GRBFN model is developed to improve the performance of GRBFN with the use of DA (Deterministic Annealing) clustering for the parameters initialization and EPSO (Evolutionary Particle Swarm Optimization) for optimizing the parameters of GRBFN. The proposed method is successfully applied to real data of ISO New England, USA. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "Recent Development in Electricity Price Forecasting Based on Computational Intelligence Techniques in Deregulated Power Market." The correction addresses errors in the original publication, specifically changing certain phrases. The correction also includes a revised section on the Adaptive Neuro-Fuzzy Inference System (ANFIS), explaining the differences between the Mamdani-based FIS approach and the Sugeno approach. The authors state that the scientific conclusions of the article are unaffected, and the correction has been approved by the Academic Editor. [Extracted from the article]

Probabilistic forecasting is an appropriate tool that helps electricity markets participants to improve their decision making. Due to changes in electricity prices, the point forecasting accuracy cannot be guaranteed. Hence, participants are more interested in results of probabilistic forecasting methods such as prediction intervals method. In this paper, a hybrid approach for probabilistic electricity price forecasting is presented. This model is based on using improved clonal selection algorithm and extreme learning machine for neural networks training process and wavelet preprocess. The wavelet is utilized to decompose data into well behaved subsets, which increases accuracy of the model. Also, due to the high required computational time for training the neural networks, autocorrelation function is used to reduce the number of neural networks inputs. Finally, in order to evaluate the proposed probabilistic forecasting method, the Ontario and Australian electricity markets data are used. [ABSTRACT FROM AUTHOR]

• A DNN based spike occurrence forecast model is proposed. • A price spike calibration model based on multi-source data is designed. • Proposes a novel data preprocess technique for electricity price forecasting. • The scheme leads to accuracy improvements that are statistically significant. With the development of the global power market reform, the monopoly of the power sector and government control pattern has gradually broken. Due to the unique properties of electricity, electricity prices show high volatility and uncertainty, bringing significant challenges to the accurate prediction of electricity prices. The sudden occurrence of a few spike prices in the electricity spot market has significantly affected electricity price forecasting accuracy. We propose a novel two-stage electricity price forecasting scheme (TSEP). A multi-source data-based spike occurrence prediction scheme is presented in the first stage, which adopts a deep neural network (DNN) to predict whether the price to be forecasted is a spike or not. Specifically, to alleviate the impact of low spike price samples, the oversampling method is used to synthesize some spikes at the data level. A loss function with a misclassification penalty to increase the cost of missing price spikes is designed at the algorithm level. Based on the outputs of the first stage, in the second stage, TSEP exploits the variance stabilizing transformations respectively suitable for pre-processing spike and normal prices and combines an artificial neural network (ANN) based spike calibration model to improve the accuracy of electricity price forecasting further. The experimental results on the European Power Exchange for France (EPEX-FR) demonstrate that TSEP increases spike occurrence prediction accuracy compared with the conventional models and significantly improves the accuracy of spike electricity price forecasting without affecting the accuracy of forecasting normal electricity price. [ABSTRACT FROM AUTHOR]

Electricity Price Forecasting (EPF) IS A branch of forecasting at the interface of electrical engineering, statistics, computer science, and finance that focuses on predicting prices in wholesale electricity markets for a whole spectrum of horizons. These range from a few minutes (real-time/intraday auctions and continuous trading); through days (day-ahead [DA] auctions); to weeks, months, or even years (exchange and over-the-counter traded futures and forward contracts). DA markets are the workhorse of power trading, particularly in Europe, and a commonly used proxy for “the electricity price.” The vast majority—up to 90%—of the EPF literature has focused on predicting DA prices. These are typically determined around noon during 24 uniform-price auctions, one for each hour of the next day. This has direct implications for the way EPF models are built. [ABSTRACT FROM AUTHOR]

This paper analyzes the potential for including jumps and cojumps in electricity price forecasting models. The study is carried out on the German–Austrian day-ahead electricity market with a multivariate framework in which each hour of the day is treated as an individual time series. Three models are specified: The ARX model, the ARX-J model (which includes jumps), and the ARX-J-CJ model (which also includes cojumps). Prices are transformed using several variance stabilizing transformations. The forecasting performance of the three models with original and transformed prices is compared using several forecast horizons running from one day-ahead to one week-ahead. Results show that the forecast horizon is crucial in determining whether jumps and cojumps should be included in electricity price forecasting. Jumps and cojumps add important information to forecast prices for horizons longer than 4 days, but there is no gain in forecast accuracy for shorter horizons. The results are of interest to market participants for taking optimal decisions and pricing base week futures contracts. • Smoothing price series is important to obtain more accurate forecasts. • Jumps and cojumps improve forecasting accuracy for horizons longer than 4 days. • Jumps and cojumps improve the forecast of week futures contracts. [ABSTRACT FROM AUTHOR]

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 L 1 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. [ABSTRACT FROM AUTHOR]

• The deficiency of the "decomposition-prediction" combination model is improved. • The model is constructed by introducing the ensemble learning. • The model is applied to the multi-step-ahead research. Research on forecasting electricity prices is of great significance to market participants. It is very difficult, however, to forecast the electricity price series because of its nonlinearity and high volatility. Considering that the existing studies directly ignore the important information contained in the residual term (Res.) after variational modal decomposition (VMD), this paper introduces a two-layer decomposition technique based on the combination of VMD technology and ensemble empirical modal decomposition (EEMD), carrying out EEMD decomposition on the residual term after VMD decomposition to improve the overall prediction accuracy of the model. At the same time, in order to address the defects of the existing hybrid model prediction methods—which use equal weights to reconstruct the prediction results—this paper draws on the idea of ensemble learning, combining the extreme learning machine (ELM) optimized by the differential evolution (DE) algorithm, introducing the DE-ELM meta-learner to optimize the reconstruction weights of the prediction components, and constructing a new hybrid model VMD-Res.-EEMD-DE-ELM-DE-ELM to obtain better prediction results. In order to verify the model's prediction performance, this paper uses electricity prices from Australian and Spanish electricity markets for empirical analysis. The results show that the hybrid model proposed in this paper has significant forecasting advantages. [ABSTRACT FROM AUTHOR]

A short-term forecasting of the electricity price with data-driven algorithms is studied in this research. A stacked denoising autoencoder (SDA) model, a class of deep neural networks, and its extended version are utilized to forecast the electricity price hourly. Data collected in Nebraska, Arkansas, Louisiana, Texas, and Indiana hubs in U.S. are utilized. Two types of forecasting, the online hourly forecasting and day-ahead hourly forecasting, are examined. In online forecasting, SDA models are compared with data-driven approaches including the classical neural networks, support vector machine, multivariate adaptive regression splines, and least absolute shrinkage and selection operator. In the day-ahead forecasting, the effectiveness of SDA models is further validated through comparing with industrial results and a recently reported method. Computational results demonstrate that SDA models are capable to accurately forecast electricity prices and the extended SDA model further improves the forecasting performance. [ABSTRACT FROM AUTHOR]