Spatiotemporal Pattern Recognition and Nonlinear PCA for Global Horizontal Irradiance Forecasting

Licciardi, Giorgio; Dambreville, Romain; Chanussot, Jocelyn; Dubost, Stéphanie

doi:10.1109/lgrs.2014.2335817

Cited by 25 publications

(19 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Yang et al [6,7] studied the forecasting of global horizontal irradiance by exponential smoothing using decompositions, while on another study, they developed the least absolute shrinkage and selection operator model using irradiance very short-term forecasting. Combining a forecasting model with GSI is important to get a better result, and forecasting GSI by the spatiotemporal pattern recognition method, ANN method, parametric models and decomposition models, has been described in [8][9][10]. Spatiotemporal pattern recognition and nonlinear principal component analysis (PCA) for global horizontal irradiance forecasting has been proposed as well by Licciardi et al [8].…”

Section: Introductionmentioning

confidence: 99%

“…Combining a forecasting model with GSI is important to get a better result, and forecasting GSI by the spatiotemporal pattern recognition method, ANN method, parametric models and decomposition models, has been described in [8][9][10]. Spatiotemporal pattern recognition and nonlinear principal component analysis (PCA) for global horizontal irradiance forecasting has been proposed as well by Licciardi et al [8]. Amrouche and Le Pivert [9] have presented an ANN based on daily local forecasting for global solar radiation, describing a novel methodology for local forecasting of daily global horizontal irradiance (GHI).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

k-Nearest Neighbor Neural Network Models for Very Short-Term Global Solar Irradiance Forecasting Based on Meteorological Data

Chen

Kartini

2017

Energies

View full text Add to dashboard Cite

This paper proposes a novel methodology for very short term forecasting of hourly global solar irradiance (GSI). The proposed methodology is based on meteorology data, especially for optimizing the operation of power generating electricity from photovoltaic (PV) energy. This methodology is a combination of k-nearest neighbor (k-NN) algorithm modelling and artificial neural network (ANN) model. The k-NN-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 GSI measurement samples was available from the PV station in Taiwan which is used as test data. The first method implements k-NN as a preprocessing technique prior to ANN method. The error statistical indicators of k-NN-ANN model the mean absolute bias error (MABE) is 42 W/m 2 and the root-mean-square error (RMSE) is 242 W/m 2 . The models forecasts are then compared to measured data and simulation results indicate that the k-NN-ANN-based model presented in this research can calculate hourly GSI with satisfactory accuracy.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

k-Nearest Neighbor Neural Network Models for Very Short-Term Global Solar Irradiance Forecasting Based on Meteorological Data

Chen

Kartini

2017

Energies

View full text Add to dashboard Cite

show abstract

“…NLPCA is commonly seen as a nonlinear generalization of principal component analysis (PCA) [10]. It can extract the linear and nonlinear principal components of the signal being processed.…”

Section: Ntroductionmentioning

confidence: 99%

Power disturbance identification based on transient behaviors using morphological max-lifting scheme and nonlinear principal component analysis

Zhang

2015

2015 IEEE Innovative Smart Grid Technologies - Asia (ISGT ASIA)

View full text Add to dashboard Cite

When a power system is contaminated by power quality problems, a short transition will appear before power system restoring to a new stability state. The short transitions of various power disturbances correspond to different principal component characteristics. According to these characteristics, in this paper, a scheme based on transient behaviors using morphological max-lifting scheme (MMLS) and nonlinear principal component analysis (NLPCA) is developed for the identification of power disturbances. PSCAD/EMTDC is applied to simulate six types of power disturbances, and massive simulation work has been conducted to demonstrate the effectiveness and feasibility of the proposed scheme.Index Terms--Morphological max-lifting scheme; nonlinear principal component analysis; power disturbance; transient behavior; identification.

show abstract

“…The ANN allows more flexible relationship load patterns with temperature. The artificial intelligence method which finds a mapping between input information and future load requires a complete historical data and proper input information [3][4]. The achievements of artificial neural network are massive fast speed, high fault tolerance and adaptive capability.…”

mentioning

confidence: 99%

“…During training, the synaptic weights get modified to model for that problem. As the network has learnt the problem it may be tested with new unknown patterns and its efficiency can be checked [3][4]. The back-propagation learning algorithm is the most frequently used method in training the networks [23][24][25][26][27], and proposed as an electrical load forecasting methodology in this paper.…”

mentioning

confidence: 99%

Application of Neural Networks to Power Systems for Electrical Load Forecasting

Singh

Gupta

2020

RJET

View full text Add to dashboard Cite

The artificial neural network system referred as parallel distributed processors conserve the information previously learnt but same time accessible to learning new information. This paper gives a brief indication of artificial neural network and its application in power system for electrical load forecasting which can further be used for enhancement of power quality. The accuracy of load forecasts has a significant effect on economy and control of power system operations for reliable and secure operation of power system. In this paper the accurate and real time data are collected from Chhattisgarh load dispatch centre of western grid of India of year 2018. This data of power flow is simulated using artificial neural network. The purpose of short-term forecasting is to satisfy as much as possible, to improve prediction accuracy.

show abstract

Spatiotemporal Pattern Recognition and Nonlinear PCA for Global Horizontal Irradiance Forecasting

Cited by 25 publications

References 14 publications

k-Nearest Neighbor Neural Network Models for Very Short-Term Global Solar Irradiance Forecasting Based on Meteorological Data

k-Nearest Neighbor Neural Network Models for Very Short-Term Global Solar Irradiance Forecasting Based on Meteorological Data

Power disturbance identification based on transient behaviors using morphological max-lifting scheme and nonlinear principal component analysis

Application of Neural Networks to Power Systems for Electrical Load Forecasting

Contact Info

Product

Resources

About