Correlation analysis on wind and hydro resources with electricity demand and prices in New Zealand

Suomalainen, Kiti; Pritchard, Geoffrey; Sharp, Basil; Yuan, Zhiquan; Zakeri, Golbon

doi:10.1016/j.apenergy.2014.10.015

Cited by 55 publications

(30 citation statements)

References 35 publications

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“…This relationship can be expressed in function form, such as Markov chains [4], regression analysis [9,10], exponential smoothing method [11], Kalman filtering method [12], ARMA model [13] and so on. Among them, the ARMA (p, q) model, used as the common statistical model, has high accuracy of analysis and prediction for stationary time series, however, due to the impact of the uncertain natural climate, wind energy has obvious trends, diversity and periodicity, which show non-stationary time series.…”

Section: Introductionmentioning

confidence: 99%

A Multi Time Scale Wind Power Forecasting Model of a Chaotic Echo State Network Based on a Hybrid Algorithm of Particle Swarm Optimization and Tabu Search

Niu

et al. 2015

Energies

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Abstract:The uncertainty and regularity of wind power generation are caused by wind resources' intermittent and randomness. Such volatility brings severe challenges to the wind power grid. The requirements for ultrashort-term and short-term wind power forecasting with high prediction accuracy of the model used, have great significance for reducing the phenomenon of abandoned wind power , optimizing the conventional power generation plan, adjusting the maintenance schedule and developing real-time monitoring systems. Therefore, accurate forecasting of wind power generation is important in electric load forecasting. The echo state network (ESN) is a new recurrent neural network composed of input, hidden layer and output layers. It can approximate well the nonlinear system and achieves great results in nonlinear chaotic time series forecasting. Besides, the ESN is simpler and less computationally demanding than the traditional neural network training, which provides more accurate training results. Aiming at addressing the disadvantages of standard ESN, this paper has made some improvements. Combined with the complementary advantages of particle swarm optimization and tabu search, the generalization of ESN is improved. To verify the validity and applicability of this method, case studies of multitime scale forecasting of wind power output are carried out to reconstruct the chaotic time series of the actual wind power generation data in a certain region to predict wind power generation. Meanwhile, the influence of seasonal factors on wind power is taken into consideration. Compared with the classical ESN and the conventional Back Propagation (BP) neural network, the results verify the superiority of the proposed method.

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Section: Introductionmentioning

confidence: 99%

A Multi Time Scale Wind Power Forecasting Model of a Chaotic Echo State Network Based on a Hybrid Algorithm of Particle Swarm Optimization and Tabu Search

Niu

et al. 2015

Energies

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show abstract

“…It can be utilized to analyze statistical interrelations between two probability variables in various fields of engineering. For example, we can study correlation of wind and hydro with electricity demand and prices [16] and develop a method to evaluate the characteristics of team communications based on social network analysis [17]. To measure the correlation and similarity between two probability variables, covariance can be utilized as follows:…”

Section: Sequential Correlation Analysismentioning

confidence: 99%

Adaptive Optimized Pattern Extracting Algorithm for Forecasting Maximum Electrical Load Duration Using Random Sampling and Cumulative Slope Index

2018

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Load forecasting techniques can be an essential method to save energy and shave peak loads in order to improve energy efficiency and maintain the stability of a power grid. To achieve this goal, machine learning-based approaches have been proposed recently. Before moving toward the long-term and ultimate solution such as machine learning, we propose a simple and efficient method to forecast electricity usage patterns and the duration of maximum electrical load using a small data set. The proposed algorithm can forecast maximum electrical load duration using random sampling and a cumulative slope index. To verify the algorithm, we utilized electricity data (from 2015.11 to 2016.12) obtained from a building with a constant lifestyle and electricity pattern. The performance of the algorithm was evaluated using electricity bills, the discharging condition of an energy storage system, and the cumulative slope index. It was found that the proposed algorithm could provide electricity cost savings of 0.62-2.28% compared with other, conventional electricity prediction techniques, such as the moving average method and exponential smoothing. In near future research, it is expected that this algorithm could be applied to electrical big data to handle real-time data processing.

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“…In Europe, on account of the 120 GW of installed wind turbines, 284 TW h of electricity are produced per year. Even though the installed capacity in Europe grows with a mean of 3% per year [2], producers have to deal with many problems related to renewable energy. In the case of wind farms, one of the most important hurdle, is the intrinsic volatility of wind speed that requires accurate evaluation of wind speed data before installing wind turbines.…”

Section: Introductionmentioning

confidence: 99%

Economic performance indicators of wind energy based on wind speed stochastic modeling

2015

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Correlation analysis on wind and hydro resources with electricity demand and prices in New Zealand

Cited by 55 publications

References 35 publications

A Multi Time Scale Wind Power Forecasting Model of a Chaotic Echo State Network Based on a Hybrid Algorithm of Particle Swarm Optimization and Tabu Search

A Multi Time Scale Wind Power Forecasting Model of a Chaotic Echo State Network Based on a Hybrid Algorithm of Particle Swarm Optimization and Tabu Search

Adaptive Optimized Pattern Extracting Algorithm for Forecasting Maximum Electrical Load Duration Using Random Sampling and Cumulative Slope Index

Economic performance indicators of wind energy based on wind speed stochastic modeling

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