One-week-ahead electricity price forecasting using weather forecasts, and its application to arbitrage in the forward market: an empirical study of the Japan Electric Power Exchange

2021 ◽  
Author(s):  
Takuji Matsumoto ◽  
Misao Endo
Keyword(s):  
Empirical Study ◽  
Electric Power ◽  
Electricity Price ◽  
Price Forecasting ◽  
Weather Forecasts ◽  
Forward Market ◽  
Power Exchange ◽  
Electricity Price Forecasting

scholarly journals Selection of Calibration Windows for Day-Ahead Electricity Price Forecasting

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2364 ◽  
Author(s):  
Grzegorz Marcjasz ◽  
Tomasz Serafin ◽  
Rafał Weron
Keyword(s):  
Empirical Study ◽  
Electricity Price ◽  
Power Markets ◽  
Price Forecasting ◽  
Prediction Errors ◽  
Major Power ◽  
Electricity Price Forecasting ◽  
Ex Ante ◽  
Expert Models ◽  
Selection Of

We conduct an extensive empirical study on the selection of calibration windows for day-ahead electricity price forecasting, which involves six year-long datasets from three major power markets and four autoregressive expert models fitted either to raw or transformed prices. Since the variability of prediction errors across windows of different lengths and across datasets can be substantial, selecting ex-ante one window is risky. Instead, we argue that averaging forecasts across different calibration windows is a robust alternative and introduce a new, well-performing weighting scheme for averaging these forecasts.

scholarly journals Short-Term Electricity Price Forecasting with a Composite Fundamental-Econometric Hybrid Methodology

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 1067 ◽  
Author(s):  
Rodrigo de Marcos ◽  
Antonio Bello ◽  
Javier Reneses
Keyword(s):  
Predictive Accuracy ◽  
Electricity Price ◽  
Price Forecasting ◽  
Short Term ◽  
Forecasting Models ◽  
Hybrid Approaches ◽  
Power Exchange ◽  
Electricity Price Forecasting ◽  
Hybrid Methodology

Various power exchanges are nowadays being affected by a plethora of factors that, as a whole, cause considerable instabilities in the system. As a result, traders and practitioners must constantly adapt their strategies and look for support for their decision-making when operating in the market. In many cases, this calls for suitable electricity price forecasting models that can account for relevant aspects for electricity price forecasting. Consequently, fundamental-econometric hybrid approaches have been developed by many authors in the literature, although these have rarely been applied in short-term contexts, where other considerations and issues must be addressed. Therefore, this work aims to develop a robust hybrid methodology that is capable of making the most of the advantages fundamental and the hybrid model in a synergistic manner, while also providing insight as to how well these models perform across the year. Several methods have been utilised in this work in order to modify the hybridisation approach and the input datasets for enhanced predictive accuracy. The performance of this proposal has been analysed in the real case study of the Iberian power exchange and has outperformed other well-recognised and traditional methods.

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.

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