Essays on the Fallacy of Electricity Price Indeterminedness: Electricity Market Stylized Facts and Conjectures of Interest Part Ia

2020 ◽  
Author(s):  
Ognjen Vukovic
Keyword(s):  
Electricity Market ◽  
Electricity Price ◽  
Stylized Facts

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 Experimental Analysis of GBM to Expand the Time Horizon of Irish Electricity Price Forecasts

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7587
Author(s):  
Conor Lynch ◽  
Christian O’Leary ◽  
Preetham Govind Kolar Sundareshan ◽  
Yavuz Akin
Keyword(s):  
Electricity Market ◽  
Electricity Consumption ◽  
Cost Effective ◽  
Absolute Error ◽  
Electricity Price ◽  
Gradient Boosting ◽  
Avant Garde ◽  
Electricity Price Forecasting ◽  
Gradient Boosting Machine ◽  
Extreme Gradient Boosting

In response to the inherent challenges of generating cost-effective electricity consumption schedules for dynamic systems, this paper espouses the use of GBM or Gradient Boosting Machine-based models for electricity price forecasting. These models are applied to data streams from the Irish electricity market and achieve favorable results, relative to the current state-of-the-art. Presently, electricity prices are published 10 h in advance of the trade day of interest. Using the forecasting methodology outlined in this paper, an estimation of these prices can be made available one day in advance of the official price publication, thus extending the time available to plan electricity utilization from the grid to be as cost effectively as possible. Extreme Gradient Boosting Machine (XGBM) models achieved a Mean Absolute Error (MAE) of 9.93 for data from 30 September 2018 to 12 December 2019 which is an 11.4% improvement on the avant-garde. LGBM models achieve a MAE score 9.58 on more recent data: the full year of 2020.

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.

Application of Intelligent Algorithm for Probability Density Estimation

2011 ◽  
Vol 186 ◽  
pp. 388-392 ◽  
Author(s):  
Hua Zheng ◽  
Li Xie ◽  
Jun Xiong
Keyword(s):  
New England ◽  
Probability Density ◽  
Density Estimation ◽  
Electricity Market ◽  
Financial Time Series ◽  
Electricity Price ◽  
Support Vector ◽  
Continuous Variables ◽  
Probability Density Estimation ◽  
Intelligent Algorithm

There is no doubt that probability distribution is primary and important for the risk analyses on financial time series. And various non-Gaussian distributions have become one of focused and unsolved problems, especially for those studies on the real continuous variables. So this paper concentrates on the intelligent algorithm for probability density estimation by Least Squares Support Vector Machines (LS-SVM), and its application on the electricity price. Moreover a practical probability density modeling of electricity price is implemented by LS-SVM. Finally, case studies on the electricity price of New England electricity market have proved the validity of the proposed model.

scholarly journals Design of Distributed Electricity Trading Mechanism Based on Contract for Difference

2021 ◽  
Vol 256 ◽  
pp. 01030
Author(s):  
Li Long ◽  
Tianhai Yang ◽  
Qifen Li ◽  
Yongwen Yang ◽  
Lifei Song ◽  
...  
Keyword(s):  
Electricity Market ◽  
Spot Market ◽  
Electricity Price ◽  
Distributed Energy ◽  
Short Term ◽  
Gradual Improvement ◽  
Price Fluctuations ◽  
Financial Contract ◽  
Trading Mechanism ◽  
Price Demand

A contract for difference is a medium and long-term financial contract, which can be used in the electricity market to lock the electricity price in advance and avoid the risk of electricity price fluctuations in the spot market. The construction of the domestic power spot market has just started. With the release of relevant policies and the gradual improvement of the market structure, it is urgent to design a corresponding trading mechanism to ensure the smooth transition of the market. The current day-ahead transactions, real-time transactions and other short-term transactions for distributed power generation, on the one hand power load forecasting, electricity price demand response and other related technologies need to be further improved, on the other hand due to the randomness and uncertainty of distributed energy, participating in the short-term spot market has large price fluctuations, which is not conducive to the stability of the electricity market, and it is also not conducive to the consumption of distributed energy. Aiming at the above problems, this paper uses the characteristics of CFDs to restrain market power to design a distributed energy trading mechanism to achieve the purpose of energy saving and emission reduction.

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