K-NN Decomposition Artificial Neural Network Models for Global Solar

@article{Chen2017KNNDA,
  title={K-NN Decomposition Artificial Neural Network Models for Global Solar},
  author={Chao Rong Chen},
  journal={International Journal of Computer and Electrical Engineering},
  year={2017},
  volume={9},
  pages={341-359}
}
  • C. Chen
  • Published 2017
  • Computer Science
  • International Journal of Computer and Electrical Engineering
This paper proposes a novel methodology for forecasting of one hourly global solar irradiance (GSI). This methodology is a combination of k-NN decompotition method and artificial neural network (ANN) algorithm modelling. The k-NN Decomposition-ANN method is designed to forecast GSI for 60 min ahead based on meteorology data for the target PV station which position is surrounded by eight other adjacent PV stations. The novelty of this method is taking into account the meteorology data. A set of… 

References

SHOWING 1-10 OF 24 REFERENCES
A hybrid method based on a new clustering technique and multilayer perceptron neural networks for hourly solar radiation forecasting
Abstract Accurate forecasting of renewable energy sources plays a key role in their integration into the grid. This paper proposes a hybrid solar irradiance forecasting framework using a
Optimization of an artificial neural network dedicated to the multivariate forecasting of daily glob
TLDR
The contribution of exogenous meteorological data (multivariate method) as time series to the authors' optimized MLP and compare with different forecasting methods: a naive forecaster (persistence), ARIMA reference predictor, an ANN with preprocessing using only endogenous inputs (univariate method), and anANN with pre processing using endogenous and exogenous inputs.
Artificial neural network based daily local forecasting for global solar radiation
When a part of the power is generated by grid connected photovoltaic installations, an effective global solar irradiation (GSI) forecasting tool becomes a must to ensure the quality and the security
Short-Term Solar Irradiance Forecasting Model Based on Artificial Neural Network Using Statistical Feature Parameters
Short-term solar irradiance forecasting (STSIF) is of great significance for the optimal operation and power predication of grid-connected photovoltaic (PV) plants. However, STSIF is very complex to
Prediction of Solar Radiation Using Artificial Neural Networks
Solar radiation data is needed by engineers, architects and scientists in the framework of studies on photovoltaic or thermal solar systems. A stochastic model for simulating global solar radiation
Forecast for surface solar irradiance at the Brazilian Northeastern region using NWP model and artificial neural networks
There has been a growing demand on energy sector for short-term predictions of energy resources to support the planning and management of electricity generation and distribution systems. The purpose
A Comparative Study of Empirical Mode Decomposition-Based Short-Term Wind Speed Forecasting Methods
Wind speed forecasting is challenging due to its intermittent nature. The wind speed time series (TS) has nonlinear and nonstationary characteristics and not normally distributed, which make it
...
1
2
3
...