A hybrid forecasting approach applied in the electrical power system based on data preprocessing, optimization and artificial intelligence algorithms

Jiang, Ping; Ma, Xuejiao

doi:10.1016/j.apm.2016.08.001

Cited by 65 publications

(25 citation statements)

References 50 publications

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“…. , IMF 8 and R n , after EMD data decomposition. The high-frequency data in highest component is removed, and the rest data are regarded as the new trend time series data.…”

Section: Resultsmentioning

confidence: 99%

“…The regression analysis process is easy, and the parameter estimation methods are complete; however, when dealing with non-linear time series data, the forecasting quality is bad and the forecasting accuracy is low. Another drawback is that it is difficult to select the influencing factors owing to the complexity of the objective data [8]. Time series forecasting aims to construct mathematical models based on the statistics of historical data, and it requires relatively small datasets and achieves a fast analysis speed, which can capture the variation trends of the recent data.…”

Section: Introductionmentioning

confidence: 99%

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Research and Application of a Hybrid Forecasting Model Based on Data Decomposition for Electrical Load Forecasting

Dong

et al. 2016

Energies

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Accurate short-term electrical load forecasting plays a pivotal role in the national economy and people's livelihood through providing effective future plans and ensuring a reliable supply of sustainable electricity. Although considerable work has been done to select suitable models and optimize the model parameters to forecast the short-term electrical load, few models are built based on the characteristics of time series, which will have a great impact on the forecasting accuracy. For that reason, this paper proposes a hybrid model based on data decomposition considering periodicity, trend and randomness of the original electrical load time series data. Through preprocessing and analyzing the original time series, the generalized regression neural network optimized by genetic algorithm is used to forecast the short-term electrical load. The experimental results demonstrate that the proposed hybrid model can not only achieve a good fitting ability, but it can also approximate the actual values when dealing with non-linear time series data with periodicity, trend and randomness.

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“…. , IMF 8 and R n , after EMD data decomposition. The high-frequency data in highest component is removed, and the rest data are regarded as the new trend time series data.…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research and Application of a Hybrid Forecasting Model Based on Data Decomposition for Electrical Load Forecasting

Dong

et al. 2016

Energies

Self Cite

View full text Add to dashboard Cite

show abstract

“…Moreover, many existing approaches use a model that fits stationary phenomena, as autoregressive-moving average [15], or adopt mathematical tools, such as Fourier transform still capturing the stationary phenomena only [16]. Some neural network topologies have been used which are adeguate only for a stationary scenario [17,18,19]. on Secondly, several approaches use aggregated data coming from power plants or consumption devices, then the corresponding model provides an average trend, while cannot perform accurate forecast for each plant, device, or place [6,8,20].…”

Section: Introductionmentioning

confidence: 99%

An advanced neural network based solution to enforce dispatch continuity in smart grids

Capizzi

Sciuto

Napoli

et al. 2018

Applied Soft Computing

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“…Meanwhile, inaccurate forecasts will lead to a direct increase of operating costs, and the The previous literature review illustrates that the hybrid forecasting model has become a trend. Some drawbacks of the models discussed above can be summarized [39,40]: (1) the traditional statistical models have a high dependence on data, a poor extrapolation effect and narrow forecasting scale, being more suitable for data featured by linear trends and unable to capture data with high fluctuation and noise. More importantly, the data of an electrical power system always features high volatility, irregularity or other tendencies due to the influence of several factors.…”

Section: Introductionmentioning

confidence: 99%

Research and Application of a Novel Hybrid Model Based on Data Selection and Artificial Intelligence Algorithm for Short Term Load Forecasting

Yang

Wang

2017

Entropy

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Abstract:Machine learning plays a vital role in several modern economic and industrial fields, and selecting an optimized machine learning method to improve time series' forecasting accuracy is challenging. Advanced machine learning methods, e.g., the support vector regression (SVR) model, are widely employed in forecasting fields, but the individual SVR pays no attention to the significance of data selection, signal processing and optimization, which cannot always satisfy the requirements of time series forecasting. By preprocessing and analyzing the original time series, in this paper, a hybrid SVR model is developed, considering periodicity, trend and randomness, and combined with data selection, signal processing and an optimization algorithm for short-term load forecasting. Case studies of electricity power data from New South Wales and Singapore are regarded as exemplifications to estimate the performance of the developed novel model. The experimental results demonstrate that the proposed hybrid method is not only robust but also capable of achieving significant improvement compared with the traditional single models and can be an effective and efficient tool for power load forecasting.

show abstract

A hybrid forecasting approach applied in the electrical power system based on data preprocessing, optimization and artificial intelligence algorithms

Cited by 65 publications

References 50 publications

Research and Application of a Hybrid Forecasting Model Based on Data Decomposition for Electrical Load Forecasting

Research and Application of a Hybrid Forecasting Model Based on Data Decomposition for Electrical Load Forecasting

An advanced neural network based solution to enforce dispatch continuity in smart grids

Research and Application of a Novel Hybrid Model Based on Data Selection and Artificial Intelligence Algorithm for Short Term Load Forecasting

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