scholarly journals Day-ahead electricity price forecasting using WPT, VMI, LSSVM-based self adaptive fuzzy kernel and modified HBMO algorithm

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rahmad Syah ◽  
Mohammad Rezaei ◽  
Marischa Elveny ◽  
Meysam Majidi Nezhad ◽  
Dadan Ramdan ◽  
...  
Keyword(s):  
Electricity Markets ◽  
Electricity Market ◽  
Input Data ◽  
Wavelet Packet ◽  
Electricity Price ◽  
Support Vector ◽  
Accurate Information ◽  
Price Forecasting ◽  
Adaptive Fuzzy ◽  
Self Adaptive

AbstractDue to focal liberality in electricity market projection, researchers try to suggest powerful and successful price forecasting algorithms. Since, the accurate information of future makes best way for market participants so as to increases their profit using bidding strategies, here suggests an algorithm for electricity price anticipation. To cover this goal, separate an algorithm into three steps, namely; pre-processing, learning and tuning. The pre-processing part consists of Wavelet Packet Transform (WPT) to analyze price signal to high and low frequency subseries and Variational Mutual Information (VMI) to select valuable input data in order to helps the learning part and decreases the computation burden. Owing to the learning part, a new Least squares support vector machine based self-adaptive fuzzy kernel (LSSVM-SFK) is proposed to extract best map pattern from input data. A new modified HBMO is introduced to optimally set LSSVM-SFK variables such as bias, weight, etc. To improve the performances of HBMO, two modifications are proposed that has high stability in HBMO. Suggested forecasting algorithm is examined on electricity markets that has acceptable efficiency than other models.

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.

Electric Price Forecast using Interbreed Approach of Linear Regression and SVM

2016 ◽  
Vol 2 (3) ◽  
pp. 537 ◽  
Author(s):  
Deepak Saini ◽  
Akash Saxena
Keyword(s):  
Linear Regression ◽  
Electricity Markets ◽  
Electrical Power ◽  
Electrical Energy ◽  
Electricity Price ◽  
Support Vector ◽  
Price Forecasting ◽  
Linear Regression Models ◽  
Price Forecast ◽  
Effective Manner

<p>Electricity price forecasting is a hypercritical issue due to the involvement of consumers and producers in electricity markets. Price forecasting plays an important role in planning and managing economic operations related with the electrical power (bidding, trading) and other decisions related with load shedding and generation rescheduling. It is also useful for optimization in electrical energy trade. This paper explores an interbreed technique based on Support Vector Machine (SVM) and linear regression to predict the day ahead electricity price using historical data as a raw insert. Different 27 linear regression models are formed to create initial framework for forecasting engine. Comparison of the performance of different forecasting engines is carried out on the basis of error indices namely Mean Square Error (MSE), Sum Square Error (SSE) and other conventional error indices. A detailed explanation of linear regression system based model is presented and simulation results exhibit that the proposed learning method is able to forecast electricity price in an effective manner.</p>

scholarly journals Electricity Price Forecasting Based on Self-Adaptive Decomposition and Heterogeneous Ensemble Learning

Energies ◽  
2020 ◽  
Vol 13 (19) ◽  
pp. 5190 ◽  
Author(s):  
Matheus Ribeiro ◽  
Stéfano Stefenon ◽  
José de Lima ◽  
Ademir Nied ◽  
Viviana Mariani ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Electricity Price ◽  
Support Vector ◽  
Forecasting Model ◽  
Price Forecasting ◽  
Decomposition Approach ◽  
Forecasting Models ◽  
Electricity Price Forecasting ◽  
Proposed Model ◽  
Self Adaptive

Electricity price forecasting plays a vital role in the financial markets. This paper proposes a self-adaptive, decomposed, heterogeneous, and ensemble learning model for short-term electricity price forecasting one, two, and three-months-ahead in the Brazilian market. Exogenous variables, such as supply, lagged prices and demand are considered as inputs signals of the forecasting model. Firstly, the coyote optimization algorithm is adopted to tune the hyperparameters of complementary ensemble empirical mode decomposition in the pre-processing phase. Next, three machine learning models, including extreme learning machine, gradient boosting machine, and support vector regression models, as well as Gaussian process, are designed with the intent of handling the components obtained through the signal decomposition approach with focus on time series forecasting. The individual forecasting models are directly integrated in order to obtain the final forecasting prices one to three-months-ahead. In this case, a grid of forecasting models is obtained. The best forecasting model is the one that has better generalization out-of-sample. The empirical results show the efficiency of the proposed model. Additionally, it can achieve forecasting errors lower than 4.2% in terms of symmetric mean absolute percentage error. The ranking of importance of the variables, from the smallest to the largest is, lagged prices, demand, and supply. This paper provided useful insights for multi-step-ahead forecasting in the electrical market, once the proposed model can enhance forecasting accuracy and stability.

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.

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.

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