A Wind Speed Correction Method Based on Modified Hidden Markov Model for Enhancing Wind Power Forecast

Li, Menglin; Yang, Ming; Yu, Yixiao; Lee, Wei-Jen

doi:10.1109/tia.2021.3127145

Cited by 86 publications

(27 citation statements)

References 32 publications

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“…In the subsequent research, more advanced and more appropriate forecasting models will be taken into account to improve the performance of each sub-model in the hybrid model. In the future, it is expected that our proposed scheme can be applied to the forecasting fields of new energy generation [17,18] and integrated loads [19][20][21].…”

Section: Discussionmentioning

confidence: 99%

A Novel Short-Term Power Load Forecasting Method Based on TSNE-EEMD-LSTM

Jiang

et al. 2022

International Transactions on Electrical Energy Systems

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In this paper, a novel power load forecasting model is proposed to fully extract the periodic characteristics of short-term load at various time scales and explore the potential correlations between influencing factors and characteristics of load components. Firstly, the t-distributed stochastic neighbor embedding algorithm is used to map sample points of high-dimensional load influencing factors to low-dimensional space, and the ensemble empirical mode decomposition algorithm is employed to split the historical load curve into multiple signal components with different frequencies. Then, several long short-term memory networks including nonlinear mapping and time series models are established to mine the relationship between low-dimensional comprehensive influencing factors and each intrinsic mode function component by utilizing different inputs. Finally, the effectiveness of the hybrid model is verified via using the short-term load dataset of 3-hour data granularity in a certain region, and the influence of key parameters of the model on the forecasting effect is discussed.

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Section: Discussionmentioning

confidence: 99%

A Novel Short-Term Power Load Forecasting Method Based on TSNE-EEMD-LSTM

Jiang

et al. 2022

International Transactions on Electrical Energy Systems

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“…Some papers in the review literature also propose to preprocess historical wind data to reduce training times, thereby achieving effective data screening and improving the accuracy of wind power prediction. [9] Jan./Feb.…”

Section: Comparative Study Of the Reviewed Wppf Models And Methodologiesmentioning

confidence: 99%

“…In order to further improve the accuracy of short-term wind power forecasting, kernel density estimation is used to estimate the probability density function of the random variables required for predictive models to avoid the density leakage problem estimated for probabilistic wind power forecasting (WPF) of a region at both the wind farm and regional levels [9][10][11]. Quantile regression (QR) approximates the conditional probability distribution of a random variable by quantiles.…”

Section: Introductionmentioning

confidence: 99%

“…Other statistical analysis methods, such as five minute-ahead wind power forecasts in terms of point forecast skill scores and calibration, 1% point analysis RL-based ESS operation strategy, empirical dynamic modeling (EDM)-based probabilistic forecast, etc. [9,[47][48][49][50][51][52][53], were introduced to improve the accuracy of ultra-short-term and short-term wind power forecasts and provide a more reliable basis for wind power grid integration.…”

Section: Introductionmentioning

confidence: 99%

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A Review of Modern Wind Power Generation Forecasting Technologies

Tsai¹,

Hong²,

Lin³

et al. 2023

Preprint

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The prediction of wind power output is part of the basic work of power grid dispatching and energy distribution. At present, the output power prediction is mainly obtained by fitting and regressing the historical data. The medium- and long-term power prediction results exhibit large deviations due to the uncertainty of wind power generation. In order to meet the demand for accessing large-scale wind power into the electricity grid and to further improve the accuracy of short-term wind power prediction, it is necessary to develop models for accurate and precise short-term wind power prediction based on advanced algorithms for studying the output power of a wind power generation system. This paper summarizes the contribution of the current advanced wind power forecasting technology and delineates the key advantages and disadvantages of various wind power forecasting models. These models have different forecasting capabilities, update the weights of each model in real time, improve the comprehensive forecasting capability of the model, and have good application prospects in wind power generation forecasting. Furthermore, the case studies and examples in the literature for accurately predicting ultra-short-term and short-term wind power generation with uncertainty and randomness are reviewed and analyzed. Finally, we present prospects for future studies that can serve as useful directions for other researchers planning to conduct similar experiments and investigations.

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“…We can apply a technique to make the most out of limited resources. One crucial aspect is ensuring that energy is used wisely and intelligent decisions are made (Louy et al, 2017;Li et al, 2022;Wang et al, 2022;Shang et al, 2023;Wang et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Scrutiny of power grids by penetrating PV energy in wind farms: a case study of the wind corridor of Jhampir, Pakistan

Soomar,

Guanghua,

Shaikh

et al. 2023

Front. Energy Res.

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This study examines the problems caused by intermittent renewable energy sources, especially wind farms, and suggests a different solar energy penetration strategy to improve their loading capacity. The study uses real-time data from a wind farm in Jhampir, Pakistan, to analyse and assess various aspects of grid stations connected to wind farms. Electrical Transient Analyzer Program is used to validate the results by linking these with actual grid system. The article focuses on creating a model for a grid connected to a wind farm and the simulation of outcomes following capacity expansion, with the installation of an autotransformer. The original capacity of the wind farm was 750 MW, which was increased to 1,250 MW, i.e., 1.66 times the actual capability. Furthermore, this capacity was further enhanced to 1,540 MW, which becomes 1.23 times the previous capacity by the penetration of a photovoltaic power plant.

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A Wind Speed Correction Method Based on Modified Hidden Markov Model for Enhancing Wind Power Forecast

Cited by 86 publications

References 32 publications

A Novel Short-Term Power Load Forecasting Method Based on TSNE-EEMD-LSTM

A Novel Short-Term Power Load Forecasting Method Based on TSNE-EEMD-LSTM

A Review of Modern Wind Power Generation Forecasting Technologies

Scrutiny of power grids by penetrating PV energy in wind farms: a case study of the wind corridor of Jhampir, Pakistan

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