Forecasting Functional Time Series with a New Hilbertian ARMAX Model: Application to Electricity Price Forecasting

Portela, José; Roque, Antonio Muñoz San

doi:10.1109/tpwrs.2017.2700287

Cited by 109 publications

(53 citation statements)

References 41 publications

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“…EPNet includes two 1D convolution layers in the CNN segment, in order to improve training efficiency, batch normalization is added after the second convolution layer of EPNet. In general, ReLU is the more widely used activation function, as shown in (11).…”

Section: Hybrid Structured Deep Neural Networkmentioning

confidence: 99%

An Electricity Price Forecasting Model by Hybrid Structured Deep Neural Networks

2018

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Electricity price is a key influencer in the electricity market. Electricity market trades by each participant are based on electricity price. The electricity price adjusted with the change in supply and demand relationship can reflect the real value of electricity in the transaction process. However, for the power generating party, bidding strategy determines the level of profit, and the accurate prediction of electricity price could make it possible to determine a more accurate bidding price. This cannot only reduce transaction risk, but also seize opportunities in the electricity market. In order to effectively estimate electricity price, this paper proposes an electricity price forecasting system based on the combination of 2 deep neural networks, the Convolutional Neural Network (CNN) and the Long Short Term Memory (LSTM). In order to compare the overall performance of each algorithm, the Mean Absolute Error (MAE) and Root-Mean-Square error (RMSE) evaluating measures were applied in the experiments of this paper. Experiment results show that compared with other traditional machine learning methods, the prediction performance of the estimating model proposed in this paper is proven to be the best. By combining the CNN and LSTM models, the feasibility and practicality of electricity price prediction is also confirmed in this paper.

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Section: Hybrid Structured Deep Neural Networkmentioning

confidence: 99%

An Electricity Price Forecasting Model by Hybrid Structured Deep Neural Networks

2018

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“…Time-series models are also popular in short-term price forecasting, which includes parsimonious stochastic models, regression or causal models and artificial intelligence (AI) models [8]. In [10], a new functional forecasting method is proposed, which attempts to generalize the standard seasonal ARMAX time-series model to the L 2 Hilbert space. A neural network (NN) model is presented in [11].…”

Section: Literature Review and Contributionsmentioning

confidence: 99%

Month ahead average daily electricity price profile forecasting based on a hybrid nonlinear regression and SVM model: an ERCOT case study

Zhong

Xie

et al. 2018

J. Mod. Power Syst. Clean Energy

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With the deregulation of the electric power industry, electricity price forecasting plays an increasingly important role in electricity markets, especially for retailors and investment decision making. Month ahead average daily electricity price profile forecasting is proposed for the first time in this paper. A hybrid nonlinear regression and support vector machine (SVM) model is proposed. Offpeak hours, peak hours in peak months and peak hours in off-peak months are distinguished and different methods are designed to improve the forecast accuracy. A nonlinear regression model with deviation compensation is proposed to forecast the prices of off-peak hours and peak hours in off-peak months. SVM is adopted to forecast the prices of peak hours in peak months. Case studies based on data from ERCOT validate the effectiveness of the proposed hybrid method.

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“…They provided evide performance, the multivariate mod one across all 12 considered data outperformed by the latter [18]. Go attempts to generalize the standard structure of the proposed model functional variables [19]. A new a methodology is divided into two integrated moving average mode moving average models per hour Neupane et al employed an ensem participates in forecasting one ho different strategies, namely, the fix each hour's expert algorithm from Alvarez et al applied a partit discover the behavior patterns of a and a teaching-learning-based opt price [23].…”

Section: Introductionmentioning

confidence: 99%

“…Gonzalez et al proposed 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 [19]. A new approach to forecast day-ahead electricity market prices, whose methodology is divided into two parts (forecasting of the electricity price through autoregressive integrated moving average models and construction of a portfolio of autoregressive integrated moving average models per hour using stochastic programming) was created by Nieta et al [20].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Electricity Price Forecasting Model Using Interval-Valued Autoregressive Process

et al. 2018

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Abstract:The uncertainty that dominates in the functioning of the electricity market is of great significance and arises, generally, because of the time imbalance in electricity consumption rates and power plants' production capacity, as well as the influence of many other factors (weather conditions, fuel costs, power plant operating costs, regulations, etc.). In this paper we try to incorporate this uncertainty in the electricity price forecasting model by applying interval numbers to express the price of electricity, with no intention of exploring influencing factors. This paper represents a hybrid model based on fuzzy C-mean clustering and the interval-valued autoregressive process for forecasting the short-term electricity price. A fuzzy C-mean algorithm was used to create interval time series to be forecasted by the interval autoregressive process. In this way, the efficiency of forecasting is improved because we predict the interval, not the crisp value where the price will be. This approach increases the flexibility of the forecasting model.

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Forecasting Functional Time Series with a New Hilbertian ARMAX Model: Application to Electricity Price Forecasting

Cited by 109 publications

References 41 publications

An Electricity Price Forecasting Model by Hybrid Structured Deep Neural Networks

An Electricity Price Forecasting Model by Hybrid Structured Deep Neural Networks

Month ahead average daily electricity price profile forecasting based on a hybrid nonlinear regression and SVM model: an ERCOT case study

Short-Term Electricity Price Forecasting Model Using Interval-Valued Autoregressive Process

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