An Optimized Heterogeneous Structure LSTM Network for Electricity Price Forecasting

Zhou, Siyu; Zhou, Lin; Mao, Mingxuan; Tai, Heng–Ming; Wan, Yihao

doi:10.1109/access.2019.2932999

Cited by 104 publications

(59 citation statements)

References 29 publications

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“…In this approach, the VMD approach is employed to extract the data features, such as high and lower frequency, but did not perform discussions about the feasibility of other features in the forecasting system. Zhou et al [23] coupled LSTM and ensemble empirical mode decomposition (EEMD) to forecasting electricity markets of Pennsylvania, New Jersey, and Maryland. Khalid et al [24] proposed an optimized deep neural network framework to conduct electricity price forecasting based on the Jaya optimizer and LSTM approach.…”

Section: Related Workmentioning

confidence: 99%

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

et al. 2020

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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.

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Section: Related Workmentioning

confidence: 99%

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

et al. 2020

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“…It reduces the number of parameters by using time relative relationship to improve training performance. It has been successfully applied in speech recognition [24], renewable energy generations forecasting [25], [26], short-term load forecasting [27]- [29]. LSTM recurrent neural network is suitable for solving the problems with the characteristics of time series correlation.…”

Section: Nomenclature ω Smentioning

confidence: 99%

Microgrid Equivalent Modeling Based on Long Short-Term Memory Neural Network

et al. 2020

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Models of electrical equipment components are the basis of the transient stability studies of power system with multi-microgrids. Microgrid is a small local power system contains different electrical components which connected into distribution network through the Point of Common Couple(PCC). In order to simplify the grid-connect model of microgrid in power system stability study, a data-driven equivalent modeling method for microgrid based on Long Short-Term Memory(LSTM) recurrent neural network is proposed in this paper. LSTM recurrent neural network is the state-of-the-art models for a variety machine learning problems, and the dynamic behaviors of microgrid under grid-connect mode are presented by the LSTM recurrent neural network. The training data set should contain the dynamic behaviors of all components, the current and power of PCC are collected as the training data set during the faults for the parameter estimation of LSTM. Based on the measurement data set, a LSTM recurrent neural network structure with four inputs and two outputs is designed. During the training process, the real and imaginary parts of the current at present and previous time are taken as the input of the network, and errors of the output active and reactive power between equivalent model and detailed model of microgrid is taken as the evaluation index of the equivalent model method. An AC microgrid contains different distributed generations and load is built in DIgSILENT, and the accuracy of the proposed method is verified by comparing the equivalent model with the original detailed system.

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“…The essential key of the MLP is to find a relationship between the input and output data [19]. Namely, when input data and corresponding desired output (also called label) are given, the mapping between them is identified using the MLP [16].…”

Section: Mlpmentioning

confidence: 99%

“…In order to enhance forecasting results, a modern technique like deep learning is also utilized for short-term SMP forecasting because of the well-known capability of deep learning to analyze non-linear data [14,15]. In addition, recurrent neural network (RNN) and also long short-term memory (LSTM) network are used as other forecasting methods because of their outstanding capability to predict time-dependent data compared to general MLP [16].…”

Section: Introductionmentioning

confidence: 99%

Dual Deep Learning Networks Based Load Forecasting with Partial Real-Time Information and Its Application to System Marginal Price Prediction

2019

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Load power forecast is one of most important tasks in power systems operation and maintenance. Enhancing its accuracy can be helpful to power systems scheduling. This paper presents how to use partial real-time temperature information in forecasting load power, which is usually done using past load power and temperature data. The partial real-time temperature information means temperature information for only part of the entire prediction time interval. To this end, a long short-term memory (LSTM) network is trained using past temperature and load power data in order to forecast load power, where forecasted load power depends on the temperature prediction implicitly. Then, in order to deal with the case where nontrivial temperature prediction errors happen, a multi-layer perceptron (MLP) network is trained using the past data describing the relation between temperature variation and load power variation. Then, the temperature is measured at the beginning of the prediction time-interval and compensated load forecast is computed by adding the output of the LSTM and that of the MLP whose input is the temperature prediction error. It is shown that the proposed compensation using the real-time temperature information indeed improves performance of load power forecast. This improved load forecast is used to predict system marginal price (SMP). The proposed method is validated using the real temperature and load power data of South Korea.

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An Optimized Heterogeneous Structure LSTM Network for Electricity Price Forecasting

Cited by 104 publications

References 29 publications

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

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

Microgrid Equivalent Modeling Based on Long Short-Term Memory Neural Network

Dual Deep Learning Networks Based Load Forecasting with Partial Real-Time Information and Its Application to System Marginal Price Prediction

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