Medium-term electricity price forecasting

Torbaghan, Shahab Shariat; Motamedi, A.; Zareipour, Hamidreza; Tuấn, Lê Anh

doi:10.1109/naps.2012.6336424

Cited by 25 publications

(22 citation statements)

References 30 publications

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“…Data-driven approaches, which take impact factors as inputs, are prevalent in mid-term price forecasting. In [19], several methods with some economic data as inputs are utilized to forecast the monthly average price, and the best mean absolute percentage error (MAPE) in these methods is 12.97%. An SVM model considering calendar day, fuel prices, electric loads, weather and import/export power is proposed in [20], and the MAPE is 8.04%.…”

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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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

View full text Add to dashboard Cite

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“…2 will guarantee to test not only every single element but also every possible combination of elements that would lead to the optimised forecasting results. The final selected input data for the proposed multiple LSSVM forecasting model at each hour t are limited to electricity hourly demand at hour t, electricity daily peak demand, electricity monthly average demand, daily price of natural gas, previous year's monthly average electricity MCP, month (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12), and hour of the day (1-24). Electricity hourly demand directly influences the electricity Fig.…”

Section: Data Collection and Pre-processmentioning

confidence: 99%

“…However, very little work has been done to forecast electricity MCP on a mid-term basis [1][2][3][4][5]. Unlike the short-term electricity MCP forecasting, the mid-term electricity MCP forecasting focuses electricity MCP on a time frame from 1 to 6 months.…”

Section: Introductionmentioning

confidence: 99%

“…Hybrid models can compensate the weaknesses of utilising any individual established method and achieve better overall system results. Torbaghan et al [5] proposed hybrid mid-term electricity monthly average price forecasting models combined with SVM/SVM, SVM/NN, NN/SVM and NN/ NN. Swief et al [17] utilised the principle component analysis and k nearest neighbour points technique to reduce the number of data entry to the SVM model.…”

Section: Introductionmentioning

confidence: 99%

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Mid‐term electricity market clearing price forecasting using multiple least squares support vector machines

Yan

Chowdhury

2014

IET Generation, Transmission & Distribution

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Mid-term electricity market clearing price (MCP) forecasting has become essential for resources reallocation, maintenance scheduling, bilateral contracting, budgeting and planning purposes. Currently, there are many techniques available for short-term electricity MCP forecasting, but very little has been done in the area of mid-term electricity MCP forecasting. A multiple least squares support vector machine (LSSVM) based mid-term electricity MCP forecasting model is proposed in this study. Data classification and price forecasting modules are designed to first pre-process the input data into corresponding price zones, and then forecast the electricity price. The proposed model showed improved forecasting accuracy on both peak prices and overall system compared to the forecasting model using a single LSSVM. PJM interconnection data are used to test the proposed model.

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“…We develop this methodology in the context of medium term electricity price forecasting, which is also relatively under-researched compared to the short term, as noted by [13]. By medium term, we consider horizons of weeks and months, over which substantial operational planning, fuel procurement, sales, and financial modelling need to be supported by forecasts and risk management.…”

Section: Introductionmentioning

confidence: 99%

Parametric Density Recalibration of a Fundamental Market Model to Forecast Electricity Prices

et al. 2016

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This paper proposes a new approach to hybrid forecasting methodology, characterized as the statistical recalibration of forecasts from fundamental market price formation models. Such hybrid methods based upon fundamentals are particularly appropriate to medium term forecasting and in this paper the application is to month-ahead, hourly prediction of electricity wholesale prices in Spain. The recalibration methodology is innovative in seeking to perform the recalibration into parametrically defined density functions. The density estimation method selects from a wide diversity of general four-parameter distributions to fit hourly spot prices, in which the first four moments are dynamically estimated as latent functions of the outputs from the fundamental model and several other plausible exogenous drivers. The proposed approach demonstrated its effectiveness against benchmark methods across the full range of percentiles of the price distribution and performed particularly well in the tails.

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Medium-term electricity price forecasting

Cited by 25 publications

References 30 publications

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

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

Mid‐term electricity market clearing price forecasting using multiple least squares support vector machines

Parametric Density Recalibration of a Fundamental Market Model to Forecast Electricity Prices

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