An Intelligent Memory Model for Short-Term Prediction: An Application to Global Solar Radiation Data

2010 ◽  
pp. 596-605
Author(s):  
Llanos Mora-Lopez ◽  
Juan Mora ◽  
Michel Piliougine ◽  
Mariano Sidrach-de-Cardona
Keyword(s):  
Solar Radiation ◽  
Global Solar Radiation ◽  
Memory Model ◽  
Short Term ◽  
Radiation Data ◽  
Intelligent Memory ◽  
Term Prediction ◽  
Short Term Prediction

scholarly journals A Hybrid LSTM-Based Genetic Programming Approach for Short-Term Prediction of Global Solar Radiation Using Weather Data

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1187
Author(s):  
Rami Al-Hajj ◽  
Ali Assi ◽  
Mohamad Fouad ◽  
Emad Mabrouk
Keyword(s):  
Solar Radiation ◽  
Solar Energy ◽  
Genetic Programming ◽  
Hybrid Model ◽  
Global Solar Radiation ◽  
Series Data ◽  
Programming Approach ◽  
Short Term ◽  
Term Prediction ◽  
Short Term Prediction

The integration of solar energy in smart grids and other utilities is continuously increasing due to its economic and environmental benefits. However, the uncertainty of available solar energy creates challenges regarding the stability of the generated power the supply-demand balance's consistency. An accurate global solar radiation (GSR) prediction model can ensure overall system reliability and power generation scheduling. This article describes a nonlinear hybrid model based on Long Short-Term Memory (LSTM) models and the Genetic Programming technique for short-term prediction of global solar radiation. The LSTMs are Recurrent Neural Network (RNN) models that are successfully used to predict time-series data. We use these models as base predictors of GSR using weather and solar radiation (SR) data. Genetic programming (GP) is an evolutionary heuristic computing technique that enables automatic search for complex solution formulas. We use the GP in a post-processing stage to combine the LSTM models’ outputs to find the best prediction of the GSR. We have examined two versions of the GP in the proposed model: a standard version and a boosted version that incorporates a local search technique. We have shown an improvement in terms of performance provided by the proposed hybrid model. We have compared its performance to stacking techniques based on machine learning for combination. The results show that the suggested method provides significant improvement in terms of performance and consistency.

scholarly journals Performance of Neural Wavelet and ANFIS Algorithms for Short-Term Prediction of Solar Radiation and Wind Velocities

2014 ◽  
Author(s):  
Mohammad Hossein Morshed Varzandeh ◽  
Omid Rahbari ◽  
Majid Vafaeipour ◽  
Kaamran Raahemifar ◽  
Fahime Heidarzadeh
Keyword(s):  
Solar Radiation ◽  
Short Term ◽  
Term Prediction ◽  
Wind Velocities ◽  
Short Term Prediction

Short-Term Prediction of Global Solar Radiation Energy Using Weather Data and Machine Learning Ensembles: A Comparative Study

2021 ◽  
Vol 143 (5) ◽  
Author(s):  
Rami Al-Hajj ◽  
Ali Assi ◽  
Mohamad Fouad
Keyword(s):  
Machine Learning ◽  
Solar Radiation ◽  
Comparative Study ◽  
Superior Performance ◽  
Weather Data ◽  
Support Vector ◽  
Short Term ◽  
Ensemble Techniques ◽  
Term Prediction ◽  
Short Term Prediction

Abstract The ability to predict solar radiation one-day-ahead is critical for the best management of renewable energy tied-grids. Several machine learning ensemble techniques have been proposed to enhance the short-term prediction of solar radiation strength. In general, finding an optimal ensemble model that consists of combining individual predictors is not trivial due to the need for tuning and other related issues. Few comparative studies have been presented to obtain optimal structures of machine learning ensemble that deal with predicting solar radiation. The contribution of the present research consists of a comparative study of various structures of stacking-based ensembles of data-driven machine learning predictors that are widely used nowadays to conclude the best stacking strategies in terms of performance to combine predictors of solar radiation. The base individual predictors are arranged to predict solar radiation intensity using historical weather and solar radiation records. Three stacking techniques, namely, feed-forward neural networks, support vector regressors, and k-nearest neighbor regressors, have been examined and compared to combine the prediction outputs of base learners. Most of the examined stacking models have been found capable to predict the solar radiation, but those related to combining heterogeneous models using neural meta-models have shown superior performance. Furthermore, we have compared the performance of combined models against recurrent models. The solar radiation predictions of the surveyed models have been evaluated and compared over an entire year. The performance enhancements provided by each alternative ensemble have been discussed.

scholarly journals Ultra-short-term Radiation Prediction based on FY-4 Satellite Cloud Images and Artificial Neural Network

2021 ◽  
Vol 293 ◽  
pp. 03017
Author(s):  
Dongyu Jia ◽  
Xiaoying Nie ◽  
Fuyuan Gao ◽  
Qingfeng Li
Keyword(s):  
Neural Network ◽  
Solar Radiation ◽  
Test Sample ◽  
Surface Radiation ◽  
Short Term ◽  
Surface Solar Radiation ◽  
Random Mutation ◽  
Term Prediction ◽  
Short Term Prediction ◽  
Combined Algorithm

Surface solar radiation is affected by many random mutation factors, which makes the ultra-short-term prediction face great challenges. In this paper, the surface radiation observation station in the northwest (Dunhuang) desert area with broad PV prospects is selected as the research object. The input parameters of the test sample are: cloud forecast value, reflectivity and brightness temperature value of a satellite cloud image closest to the forecast time. The MATLAB software is used to model the prediction program and to predict the surface solar radiation in the next 10 minutes. A combined algorithm of satellite cloud images and neural network is applied to predict surface solar radiation for the next 10 minutes and is compared with the measured surface solar radiation. The model is a lightweight calculation model, it satisfies the calculation precision of engineering requirements. The results show that the diurnal variation trend of measured and predicted radiation values is basically the same. Among them, the prediction accuracy of the model for cloudy days is higher, while for snowy days with more abrupt changes, the prediction error of abrupt points is larger. The model can provide reference for ultra-short-term prediction of surface radiation.

Short–Term Prediction of Available Parking Space Based on Improved Wavelet Neural Network

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Jinfen Wang ◽  
Xiaofei Ye ◽  
Zhen Yang ◽  
Qiming Ye ◽  
Chang Yang
Keyword(s):  
Neural Network ◽  
Wavelet Neural Network ◽  
Parking Space ◽  
Short Term ◽  
Term Prediction ◽  
Short Term Prediction

Analysis of Spatial Smoothing Effect of Short-term Fluctuation of Global Solar Radiation based on the Data Including Quantization Error

2011 ◽  
Vol 131 (5) ◽  
pp. 413-420 ◽  
Author(s):  
Yosuke Ue ◽  
Ryoichi Hara ◽  
Hiroyuki Kita ◽  
Yutaka Saito ◽  
Katsuyuki Takitani ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Global Solar Radiation ◽  
Quantization Error ◽  
Smoothing Effect ◽  
Spatial Smoothing ◽  
Short Term ◽  
Term Fluctuation

Development of a Technique for Short-Term Prediction of Hydrometeors Using Advection and Physical Forcing.

1983 ◽  
Author(s):  
Gregory S. Forbes ◽  
John J. Cahir ◽  
Paul B. Dorian ◽  
Walter D. Lottes ◽  
Kathy Chapman
Keyword(s):  
Short Term ◽  
Physical Forcing ◽  
Term Prediction ◽  
Short Term Prediction
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