scholarly journals An optimized model of electricity price forecasting in the electricity market based on fuzzy timeseries

2014 ◽  
Vol 2 (1) ◽  
pp. 677-683 ◽  
Author(s):  
Behrooz Safarinejadian ◽  
Masihollah Gharibzadeh ◽  
Mohsen Rakhshan
Keyword(s):  
Electricity Market ◽  
Electricity Price ◽  
Price Forecasting ◽  
Electricity Price Forecasting ◽  
Optimized Model

scholarly journals Short-Term Electricity Price Forecasting by Employing Ensemble Empirical Mode Decomposition and Extreme Learning Machine

Forecasting ◽  
2021 ◽  
Vol 3 (3) ◽  
pp. 460-477
Author(s):  
Sajjad Khan ◽  
Shahzad Aslam ◽  
Iqra Mustafa ◽  
Sheraz Aslam
Keyword(s):  
Extreme Learning Machine ◽  
Empirical Mode Decomposition ◽  
Electricity Market ◽  
Ensemble Empirical Mode Decomposition ◽  
Electricity Price ◽  
Price Forecasting ◽  
Short Term ◽  
Mode Decomposition ◽  
Electricity Price Forecasting ◽  
Learning Machine

Day-ahead electricity price forecasting plays a critical role in balancing energy consumption and generation, optimizing the decisions of electricity market participants, formulating energy trading strategies, and dispatching independent system operators. Despite the fact that much research on price forecasting has been published in recent years, it remains a difficult task because of the challenging nature of electricity prices that includes seasonality, sharp fluctuations in price, and high volatility. This study presents a three-stage short-term electricity price forecasting model by employing ensemble empirical mode decomposition (EEMD) and extreme learning machine (ELM). In the proposed model, the EEMD is employed to decompose the actual price signals to overcome the non-linear and non-stationary components in the electricity price data. Then, a day-ahead forecasting is performed using the ELM model. We conduct several experiments on real-time data obtained from three different states of the electricity market in Australia, i.e., Queensland, New South Wales, and Victoria. We also implement various deep learning approaches as benchmark methods, i.e., recurrent neural network, multi-layer perception, support vector machine, and ELM. In order to affirm the performance of our proposed and benchmark approaches, this study performs several performance evaluation metric, including the Diebold–Mariano (DM) test. The results from the experiments show the productiveness of our developed model (in terms of higher accuracy) over its counterparts.

scholarly journals Neural Network Based Model Comparison for Intraday Electricity Price Forecasting

Energies ◽  
2019 ◽  
Vol 12 (23) ◽  
pp. 4557 ◽  
Author(s):  
Ilkay Oksuz ◽  
Umut Ugurlu
Keyword(s):  
Neural Networks ◽  
Electricity Markets ◽  
Electricity Market ◽  
Network Architecture ◽  
Model Comparison ◽  
Market Price ◽  
Absolute Error ◽  
Electricity Price ◽  
Price Forecasting ◽  
Electricity Price Forecasting

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.

Short-Term Electricity Price Forecasting Based on MPSO-BP Algorithm

2012 ◽  
Vol 591-593 ◽  
pp. 1351-1355 ◽  
Author(s):  
Yu Dong ◽  
Qiang Yang ◽  
Wen Jun Yan
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Electricity Market ◽  
Optimal Solution ◽  
Electricity Price ◽  
Convergence Time ◽  
Price Forecasting ◽  
Short Term ◽  
Electricity Price Forecasting ◽  
Improved Performance

In this paper, we exploited the short-term electricity price forecasting issue by introducing a global search mechanism based on the improved particle swarm optimization (MPSO) algorithm for the neural network training. The proposed MPSO algorithm is used for the initial weights and threshold of BP neural network in the process of optimization. We then proposed a novel short-term electricity price forecasting model based on MPSO-BP neural network. The paper provides a number of examples of bidding model of the California electricity market to forecasting market clear price using BP neural network trained by MPSO. Through the comparative study of the conventional BP neural network and the proposed MPSO-BP neural network, the proposed method demonstrates improved performance in finding the optimal solution with excellent convergence time for all the simulated scenarios.

Day Ahead Electricity Price Forecasting in Coupled Markets

2021 ◽  
pp. 1-29
Author(s):  
Konstantinia Daskalou ◽  
Christina Diakaki
Keyword(s):  
Neural Network ◽  
European Union ◽  
Artificial Neural Network ◽  
Electricity Market ◽  
Market Integration ◽  
Electricity Price ◽  
Price Forecasting ◽  
The European Union ◽  
Electricity Price Forecasting ◽  
Market Coupling

Day ahead electricity price forecasting is an extensively studied problem, and several statistical, intelligence-based, and other techniques have been proposed in literature to address it. However, the liberalization of the electricity market taking place during the last decades and the market coupling pursued within the European Union reshape the problem and create the need to confirm the effectiveness and/or revise existing methods and solution techniques, and/or invent new approaches. Given that complete integration has not achieved yet, both relevant data and studies of forecasting considering integration are still rather sparse. It has thus been the aim of this chapter to contribute to filling this gap by focusing on and studying the market integration effects in day ahead electricity price forecasting. To this end, an artificial neural network has been developed and used under several, with respect to inputs, forecasting scenarios considering the Italian electricity market.

scholarly journals The Combination Forecasting of Electricity Price Based on Price Spikes Processing: A Case Study in South Australia

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Jianzhou Wang ◽  
Ling Xiao ◽  
Jun Shi
Keyword(s):  
Electricity Market ◽  
Spatial Clustering ◽  
South Australia ◽  
Linear Interpolation ◽  
Electricity Price ◽  
Price Forecasting ◽  
Combined Model ◽  
Electricity Price Forecasting ◽  
Combination Forecasting

Electricity price forecasting holds very important position in the electricity market. Inaccurate price forecasting may cause energy waste and management chaos in the electricity market. However, electricity price forecasting has always been regarded as one of the largest challenges in the electricity market because it shows high volatility, which makes electricity price forecasting difficult. This paper proposes the use of artificial intelligence optimization combination forecasting models based on preprocessing data, called “chaos particles optimization (CPSO) weight-determined combination models.” These models allow for the weight of the combined model to take values of[-1,1]. In the proposed models, the density-based spatial clustering of applications with noise (DBSCAN) algorithm is used to identify outliers, and the outliers are replaced by a new data-produced linear interpolation function. The proposed CPSO weight-determined combination models are then used to forecast the projected future electricity price. In this case study, the electricity price data of South Australia are simulated. The results indicate that, while the weight of the combined model takes values of[-1,1], the proposed combination model can always provide adaptive, reliable, and comparatively accurate forecast results in comparison to traditional combination models.

Particle Swarm Optimization Based GM(1,1) Method on Short-Term Electricity Price Forecasting with Predicted Error Improvement

2010 ◽  
Vol 40-41 ◽  
pp. 183-188
Author(s):  
Rui Qing Wang ◽  
Fu Xiong Wang ◽  
Wen Tian Ji
Keyword(s):  
Particle Swarm Optimization ◽  
Electricity Market ◽  
Particle Swarm ◽  
Electricity Price ◽  
Price Forecasting ◽  
Short Term ◽  
Swarm Optimization ◽  
Forecasting Accuracy ◽  
Market Participants ◽  
Electricity Price Forecasting

Under deregulated environment, accurate electricity price forecasting is a crucial issue concerned by all market participants. Experience shows that single forecasting model is very difficult to improve the forecasting accuracy due to the complicated factors affecting electricity prices. In this paper, a particle swarm optimization based GM(1,1) method on short-term electricity price forecasting with predicted error improvement is proposed, in which the moving average method is used to process the raw data, the particle swarm optimization based GM(1,1) model is used to the processed series, and the time series analysis is used to further improve the predicted errors. The numerical example based on the historical data of the PJM market shows that the method can reflect the characteristics of electricity price better and the forecasting accuracy can be improved virtually compared with the conventional GM(1,1) model. The forecasted prices accurate enough to be used by electricity market participants to prepare their bidding strategies.

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