Verification of Numerical Weather Prediction Model Results for Energy Applications in Latvia

Sīle, Tija; Beķere, L.; Cepite-Frisfelde, Daiga; Seņņikovs, Juris; Bethers, Uldis

doi:10.1016/j.egypro.2014.10.369

Cited by 13 publications

(3 citation statements)

References 8 publications

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“…On the basis of frequently used assessment criteria, the higher horizontal resolution model produced a slightly better effect, especially for 27 to 48 h advanced forecasting. Sile et al [10] supported the advantages of NWP strategies for meteorological forecasting, which were considered credible when we conducted our wind resource assessment. In this work, the forecasting results were verified based on data from May and November 2013.…”

Section: Introductionmentioning

confidence: 55%

Integrated Forecasting Method for Wind Energy Management: A Case Study in China

et al. 2019

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Wind speed forecasting helps to increase the efficacy of wind farms and prompts the comparative superiority of wind energy in the global electricity system. Many wind speed forecasting theories have been widely applied to forecast wind speed, which is nonlinear, and unstable. Current forecasting strategies can be applied to various wind speed time series. However, some models neglect the prerequisite of data preprocessing and the objective of simultaneously optimizing accuracy and stability, which results in poor forecast. In this research, we developed a combined wind speed forecasting strategy that includes several components: data pretreatment, optimization, forecasting, and assessment. The developed system remedies some deficiencies in traditional single models and markedly enhances wind speed forecasting performance. To evaluate the performance of this combined strategy, 10-min wind speed sequences gathered from large wind farms in Shandong province in China were adopted as a case study. The simulation results show that the forecasting ability of our proposed combined strategy surpasses the other selected comparable models to some extent. Thus, the model can provide reliable support for wind power generation scheduling.

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Section: Introductionmentioning

confidence: 55%

Integrated Forecasting Method for Wind Energy Management: A Case Study in China

et al. 2019

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“…The results demonstrated that the weather data provided by NWP models led to performance improvements. Sile et al [18] argued that NWP models were a reliable source of meteorological forecasts and could also be used in wind resource assessment. They also analysed the influence of wind speed and wind direction on model errors.…”

Section: Physical Forecasting Methodsmentioning

confidence: 99%

Comparative Study of Hybrid Models Based on a Series of Optimization Algorithms and Their Application in Energy System Forecasting

Liu

2016

Energies

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Abstract:Big data mining, analysis, and forecasting play vital roles in modern economic and industrial fields, especially in the energy system. Inaccurate forecasting may cause wastes of scarce energy or electricity shortages. However, forecasting in the energy system has proven to be a challenging task due to various unstable factors, such as high fluctuations, autocorrelation and stochastic volatility. To forecast time series data by using hybrid models is a feasible alternative of conventional single forecasting modelling approaches. This paper develops a group of hybrid models to solve the problems above by eliminating the noise in the original data sequence and optimizing the parameters in a back propagation neural network. One of contributions of this paper is to integrate the existing algorithms and models, which jointly show advances over the present state of the art. The results of comparative studies demonstrate that the hybrid models proposed not only satisfactorily approximate the actual value but also can be an effective tool in the planning and dispatching of smart grids.

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“…It has often been employed for energy management, unit commitment and load dispatch [6][7][8][9][10][11][12][13][14]. The high accuracy of load forecasting guarantees the safe and stable operation of power systems [15][16][17]. Therefore, it is necessary to improve the reliability and the forecasting accuracy of smart grids [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Research and Application of Hybrid Forecasting Model Based on an Optimal Feature Selection System—A Case Study on Electrical Load Forecasting

Dong

Wang

et al. 2017

Energies

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Abstract:The process of modernizing smart grid prominently increases the complexity and uncertainty in scheduling and operation of power systems, and, in order to develop a more reliable, flexible, efficient and resilient grid, electrical load forecasting is not only an important key but is still a difficult and challenging task as well. In this paper, a short-term electrical load forecasting model, with a unit for feature learning named Pyramid System and recurrent neural networks, has been developed and it can effectively promote the stability and security of the power grid. Nine types of methods for feature learning are compared in this work to select the best one for learning target, and two criteria have been employed to evaluate the accuracy of the prediction intervals. Furthermore, an electrical load forecasting method based on recurrent neural networks has been formed to achieve the relational diagram of historical data, and, to be specific, the proposed techniques are applied to electrical load forecasting using the data collected from New South Wales, Australia. The simulation results show that the proposed hybrid models can not only satisfactorily approximate the actual value but they are also able to be effective tools in the planning of smart grids.

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Verification of Numerical Weather Prediction Model Results for Energy Applications in Latvia

Cited by 13 publications

References 8 publications

Integrated Forecasting Method for Wind Energy Management: A Case Study in China

Integrated Forecasting Method for Wind Energy Management: A Case Study in China

Comparative Study of Hybrid Models Based on a Series of Optimization Algorithms and Their Application in Energy System Forecasting

Research and Application of Hybrid Forecasting Model Based on an Optimal Feature Selection System—A Case Study on Electrical Load Forecasting

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