Data analysis and short term load forecasting in Iran electricity market using singular spectral analysis (SSA)

Afshar, K.; Bigdeli, Nooshin

doi:10.1016/j.energy.2011.02.003

Cited by 81 publications

(32 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…. + a 167 P 506 (17) Now, for forecasting the electricity price at the second hour of the test week, which corresponds to the 674rd, one will havê P 674 = a 1P673 + a 2 P 672 + . .…”

Section: Results Of Nem Electricity Price Forecastingmentioning

confidence: 99%

Day‐ahead electricity price analysis and forecasting by singular spectrum analysis

Miranian

Abdollahzade

Hassani

2013

IET Generation, Transmission & Distribution

View full text Add to dashboard Cite

This study proposes a model-free approach for day-ahead electricity price forecasting. The proposed approached is based on the singular spectrum analysis (SSA) technique. The SSA is a relatively new and powerful technique in time series analysis and forecasting thanks to its well-known capabilities in extracting the main structure of the broad classes of the time series. In this study, it is shown that SSA can be employed to decompose the original electricity price series into trend, periodic and noisy components. The main part of the price series, that is, the trend and harmonic components, is reconstructed by removing the noise component from the original series. The reconstructed price series is then used for forecasting the day-ahead electricity prices. The proposed approach is evaluated by analysing and forecasting of the dayahead electricity prices in the Australian and Spanish electricity markets. The forecasting results confirm the superiority of the SSA approach compared with some of the recently published forecasting techniques.

show abstract

“…. + a 167 P 506 (17) Now, for forecasting the electricity price at the second hour of the test week, which corresponds to the 674rd, one will havê P 674 = a 1P673 + a 2 P 672 + . .…”

Section: Results Of Nem Electricity Price Forecastingmentioning

confidence: 99%

Day‐ahead electricity price analysis and forecasting by singular spectrum analysis

Miranian

Abdollahzade

Hassani

2013

IET Generation, Transmission & Distribution

View full text Add to dashboard Cite

show abstract

“…The applications of this technique are many and various, for example, in meteorology, physics, economics, and financial mathematics. In recent years, SSA has been designed and employed to various practical problems, for instance, in industrial production [22], by Afshar et al [23] to load forecasting in the electricity market as well as other markets, for forecasting CO 2 emissions [24], and for signal extraction in a genetics related application [25]. Moreover, a forecasting method was presented with Artificial Neural Network and SSA [26] and a hybrid model Coupled with SSA was used to forecast rainfall [27].…”

Section: Ssamentioning

confidence: 99%

“…Comprehensive surveys of these datasets and comparisons between them can be found in [23,[25][26][27]]. In the current study, we use the ProGen standard dataset.…”

Section: Fictitious Project Datamentioning

confidence: 99%

Designing a Framework to Improve Time Series Data of Construction Projects: Application of a Simulation Model and Singular Spectrum Analysis

2016

View full text Add to dashboard Cite

During a construction project life cycle, project costs and time estimations contribute greatly to baseline scheduling. Besides, schedule risk analysis and project control are also influenced by the above factors. Although many papers have offered estimation techniques, little attempt has been made to generate project time series data as daily progressive estimations in different project environments that could help researchers in generating general and customized formulae in further studies. This paper, however, is an attempt to introduce a new simulation approach to reflect the data regarding time series progress of the project, considering the specifications and the complexity of the project and the environment where the project is performed. Moreover, this simulator can equip project managers with estimated information, which reassures them of the execution stages of the project although they lack historical data. A case study is presented to show the usefulness of the model and its applicability in practice. In this study, singular spectrum analysis has been employed to analyze the simulated outputs, and the results are separated based on their signal and noise trends. The signal trend is used as a point-of-reference to compare the outputs of a simulation employing S-curve technique results and the formulae corresponding to earned value management, as well as the life of a given project.

show abstract

“…For example, Bianco et al [2] proposed linear regression models for electricity consumption forecasting; Zhou et al [3] applied a grey prediction model for energy consumption; Afshar and Bigdeli [4] proposed an improved singular spectral analysis method for short-term load forecasting (STLF) for the Iranian electricity market; and Kumar and Jain [5] applied three time series models-Grey-Markov model, Grey-Model with rolling mechanism, and singular spectrum analysis-to forecast the consumption of conventional energy in India. By employing artificial neural networks, references [6][7][8][9] proposed several useful short-term load forecasting models.…”

Section: Introductionmentioning

confidence: 99%

Support Vector Regression Model Based on Empirical Mode Decomposition and Auto Regression for Electric Load Forecasting

et al. 2013

View full text Add to dashboard Cite

Electric load forecasting is an important issue for a power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non-linear learning capability of support vector regression (SVR), this paper presents a SVR model hybridized with the empirical mode decomposition (EMD) method and auto regression (AR) for electric load forecasting. The electric load data of the New South Wales (Australia) market are employed for comparing the forecasting performances of different forecasting models. The results confirm the validity of the idea that the proposed model can simultaneously provide forecasting with good accuracy and interpretability.

show abstract

Data analysis and short term load forecasting in Iran electricity market using singular spectral analysis (SSA)

Cited by 81 publications

References 21 publications

Day‐ahead electricity price analysis and forecasting by singular spectrum analysis

Day‐ahead electricity price analysis and forecasting by singular spectrum analysis

Designing a Framework to Improve Time Series Data of Construction Projects: Application of a Simulation Model and Singular Spectrum Analysis

Support Vector Regression Model Based on Empirical Mode Decomposition and Auto Regression for Electric Load Forecasting

Contact Info

Product

Resources

About