Dynamic Neural Network Based Very Short-Term Wind Speed Forecasting
TL;DR: The result shows that the proposed model outperforms the BPNN based on the metrics used, and the nonlinear autoregressive model with exogenous inputs (NARX) is proposed for wind speed forecast.
Abstract: In this paper, the nonlinear autoregressive model with exogenous inputs (NARX) is proposed for wind speed forecast. Forecasting wind speed is a challenging task in wind energy research domain which influences the dynamic control of wind turbine and for system scheduling. The aim of this study is to obtain suitable forecast model for wind speed with time series input variables such as wind direction, humidity, pressure and time. The meteorological data observed with 15 minute time intervals is used for the model and the performance is evaluated and compared with the back propagation neural network (BPNN). The result shows that the proposed model outperforms the BPNN based on the metrics used.
Citations
More filters
TL;DR: The results on test set show the correction considering inherent errors of numerical techniques can integrate the physical with statistical information effectively and enhance the forecast accuracy indeed.
87 citations
TL;DR: A novel combined model for wind speed forecasting, which combined hybrid models based on decomposition method and optimization algorithm, and ESN (Echo state network) is initially applied to integrate all the results obtained by each hybrid model and achieve the ultimate forecasting results.
56 citations
TL;DR: An accurate prediction of wind speed based on a Recursive Radial Basis Function Neural Network possessing the three inputs of wind direction, temperature and wind speed to improve modern power system protection, control and management is proposed.
Abstract: Environmental considerations have prompted the use of renewable energy resources worldwide for reduction of greenhouse gas emissions. An accurate prediction of wind speed plays a major role in environmental planning, energy system balancing, wind farm operation and control, power system planning, scheduling, storage capacity optimization, and enhancing system reliability. This paper proposes an accurate prediction of wind speed based ona Recursive Radial Basis Function Neural Network (RRBFNN) possessing the three inputs of wind direction, temperature and wind speed to improve modern power system protection, control and management. Simulation results confirm that the proposed model improves the wind speed prediction accuracy with least error when compared with other existing prediction models.
41 citations
Cites background from "Dynamic Neural Network Based Very S..."
...Ramesh Babu N and Arulmozhivarman P [11], develop a very short-term forecasting model based on Nonlinear Auto regression with exogenous input (NARX), and achieves 0....
[...]
26 citations
TL;DR: An algorithm for the detection of unexpected stack voltage deviations in an Solid Oxide Fuel Cells (SOFC)-based power system with multiple stacks is presented and its validation in a simulated online environment is validated.
19 citations
References
More filters
TL;DR: It is rigorously established that standard multilayer feedforward networks with as few as one hidden layer using arbitrary squashing functions are capable of approximating any Borel measurable function from one finite dimensional space to another to any desired degree of accuracy, provided sufficiently many hidden units are available.
18,794 citations
TL;DR: It is demonstrated that finite linear combinations of compositions of a fixed, univariate function and a set of affine functionals can uniformly approximate any continuous function ofn real variables with support in the unit hypercube.
Abstract: In this paper we demonstrate that finite linear combinations of compositions of a fixed, univariate function and a set of affine functionals can uniformly approximate any continuous function ofn real variables with support in the unit hypercube; only mild conditions are imposed on the univariate function. Our results settle an open question about representability in the class of single hidden layer neural networks. In particular, we show that arbitrary decision regions can be arbitrarily well approximated by continuous feedforward neural networks with only a single internal, hidden layer and any continuous sigmoidal nonlinearity. The paper discusses approximation properties of other possible types of nonlinearities that might be implemented by artificial neural networks.
12,286 citations
TL;DR: In this paper, the authors present a state-of-the-art survey of ANN applications in forecasting and provide a synthesis of published research in this area, insights on ANN modeling issues, and future research directions.
3,680 citations
TL;DR: A bibliographical survey on the general background of research and developments in the fields of wind speed and wind power forecasting and further direction for additional research and application is proposed.
Abstract: In the world, wind power is rapidly becoming a generation technology of significance. Unpredictability and variability of wind power generation is one of the fundamental difficulties faced by power system operators. Good forecasting tools are urgent needed under the relevant issues associated with the integration of wind energy into the power system. This paper gives a bibliographical survey on the general background of research and developments in the fields of wind speed and wind power forecasting. Based on the assessment of wind power forecasting models, further direction for additional research and application is proposed.
1,073 citations
"Dynamic Neural Network Based Very S..." refers methods in this paper
...Several methods are proposed by researchers [5, 6] for wind speed forecast and neural networks are best suitable for this application [7]....
[...]