scholarly journals Prediction of Solar Radiation Intensity using Extreme Learning Machine

2018 ◽  
Vol 12 (2) ◽  
pp. 691 ◽  
Author(s):  
Hadi Suyono ◽  
Hari Santoso ◽  
Rini Nur Hasanah ◽  
Unggul Wibawa ◽  
Ismail Musirin
Keyword(s):  
Artificial Intelligence ◽  
Solar Radiation ◽  
Extreme Learning Machine ◽  
Training Data ◽  
Good Prediction ◽  
Statistical Probability ◽  
Solar Radiation Intensity ◽  
Good Prediction Accuracy ◽  
Weather And Climate ◽  
Learning Machine

The generated energy capacity at a solar power plant depends on the availability of solar radiation. In some regions, solar radiation is not always available throughout the day, or even week, depending on the weather and climate in the area. To be able to produce energy optimally throughout the year, the availability of solar radiation needs to be predicted based on the weather and climate behavior data. Many methods have been so far used to predict the availability of solar radiation, either by mathematical approach, statistical probability, or even artificial intelligence-based methods. This paper describes a method of predicting the availability of solar radiation using the Extreme Learning Machine (ELM) method. It is based on the artificial intelligence methods and known to have a good prediction accuracy. To measure the performance of the ELM method, a conventional forecasting method using the Multiple Linear Regression (MLR) method has been used as a comparison. The implementation of both the ELM and MLR methods has been tested using the solar radiation data of the Basel City, Switzerland, which are available to public. Five years of data have been divided into training data and testing data for 6 case-studies considered. Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) have been used as the parameters to measure the prediction results based on the actual data analysis. The results show that the obtained average values of RMSE and MAE by using the ELM method respectively are 122.45 W/m<sup>2</sup> and 84.04 W/m<sup>2</sup>, while using the MLR method they are 141.18 W/m<sup>2</sup> and 104.87 W/m<sup>2</sup> respectively. It means that the ELM method proved to perform better than the MLR method, giving 15.29% better value of RMSE parameter and 24.79% better value of MAE parameter.

scholarly journals Prediction and Stability Assessment of Soft Foundation Settlement of the Fishbone-Shaped Dike Near the Estuary of the Yangtze River Using Machine Learning Methods

2021 ◽  
Vol 13 (7) ◽  
pp. 3744
Author(s):  
Mingcheng Zhu ◽  
Shouqian Li ◽  
Xianglong Wei ◽  
Peng Wang
Keyword(s):  
Extreme Learning Machine ◽  
Prediction Models ◽  
Good Prediction ◽  
Foundation Settlement ◽  
Machine Method ◽  
Settlement Prediction ◽  
Soft Foundation ◽  
Simulation Results ◽  
Learning Machine ◽  
The Stability

Fishbone-shaped dikes are always built on the soft soil submerged in the water, and the soft foundation settlement plays a key role in the stability of these dikes. In this paper, a novel and simple approach was proposed to predict the soft foundation settlement of fishbone dikes by using the extreme learning machine. The extreme learning machine is a single-hidden-layer feedforward network with high regression and classification prediction accuracy. The data-driven settlement prediction models were built based on a small training sample size with a fast learning speed. The simulation results showed that the proposed methods had good prediction performances by facilitating comparisons of the measured data and the predicted data. Furthermore, the final settlement of the dike was predicted by using the models, and the stability of the soft foundation of the fishbone-shaped dikes was assessed based on the simulation results of the proposed model. The findings in this paper suggested that the extreme learning machine method could be an effective tool for the soft foundation settlement prediction and assessment of the fishbone-shaped dikes.

scholarly journals Global Solar Radiation Prediction Using Hybrid Online Sequential Extreme Learning Machine Model

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3415 ◽  
Author(s):  
Muzhou Hou ◽  
Tianle Zhang ◽  
Futian Weng ◽  
Mumtaz Ali ◽  
Nadhir Al-Ansari ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Extreme Learning Machine ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Renewable Energy Sources ◽  
Information Criterion ◽  
Absolute Error ◽  
Global Solar Radiation ◽  
Machine Model ◽  
Learning Machine

Accurate global solar radiation prediction is highly essential for related research on renewable energy sources. The cost implication and measurement expertise of global solar radiation emphasize that intelligence prediction models need to be applied. On the basis of long-term measured daily solar radiation data, this study uses a novel regularized online sequential extreme learning machine, integrated with variable forgetting factor (FOS-ELM), to predict global solar radiation at Bur Dedougou, in the Burkina Faso region. Bayesian Information Criterion (BIC) is applied to build the seven input combinations based on speed (Wspeed), maximum and minimum temperature (Tmax and Tmin), maximum and minimum humidity (Hmax and Hmin), evaporation (Eo) and vapor pressure deficiency (VPD). For the difference input parameters magnitudes, seven models were developed and evaluated for the optimal input combination. Various statistical indicators were computed for the prediction accuracy examination. The experimental results of the applied FOS-ELM model demonstrated a reliable prediction accuracy against the classical extreme learning machine (ELM) model for daily global solar radiation simulation. In fact, compared to classical ELM, the FOS-ELM model reported an enhancement in the root mean square error (RMSE) and mean absolute error (MAE) by (68.8–79.8%). In summary, the results clearly confirm the effectiveness of the FOS-ELM model, owing to the fixed internal tuning parameters.

Multiple-Instance Learning via an RBF Kernel-Based Extreme Learning Machine

2017 ◽  
Vol 26 (1) ◽  
pp. 185-195 ◽  
Author(s):  
Jie Wang ◽  
Liangjian Cai ◽  
Xin Zhao
Keyword(s):  
Extreme Learning Machine ◽  
State Of The Art ◽  
Multiple Instance Learning ◽  
Training Data ◽  
Data Sets ◽  
Rbf Kernel ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine ◽  
Hidden Layer ◽  
Instance Space

AbstractAs we are usually confronted with a large instance space for real-word data sets, it is significant to develop a useful and efficient multiple-instance learning (MIL) algorithm. MIL, where training data are prepared in the form of labeled bags rather than labeled instances, is a variant of supervised learning. This paper presents a novel MIL algorithm for an extreme learning machine called MI-ELM. A radial basis kernel extreme learning machine is adapted to approach the MIL problem using Hausdorff distance to measure the distance between the bags. The clusters in the hidden layer are composed of bags that are randomly generated. Because we do not need to tune the parameters for the hidden layer, MI-ELM can learn very fast. The experimental results on classifications and multiple-instance regression data sets demonstrate that the MI-ELM is useful and efficient as compared to the state-of-the-art algorithms.

OMP-ELM: Orthogonal Matching Pursuit-Based Extreme Learning Machine for Regression

2015 ◽  
Vol 24 (1) ◽  
pp. 135-143 ◽  
Author(s):  
Omer F. Alcin ◽  
Abdulkadir Sengur ◽  
Jiang Qian ◽  
Melih C. Ince
Keyword(s):  
Extreme Learning Machine ◽  
Matching Pursuit ◽  
Computational Cost ◽  
Machine Intelligence ◽  
Orthogonal Matching Pursuit ◽  
Training Data ◽  
Singularity Problem ◽  
Data Set ◽  
Learning Machine ◽  
Hidden Layer

AbstractExtreme learning machine (ELM) is a recent scheme for single hidden layer feed forward networks (SLFNs). It has attracted much interest in the machine intelligence and pattern recognition fields with numerous real-world applications. The ELM structure has several advantages, such as its adaptability to various problems with a rapid learning rate and low computational cost. However, it has shortcomings in the following aspects. First, it suffers from the irrelevant variables in the input data set. Second, choosing the optimal number of neurons in the hidden layer is not well defined. In case the hidden nodes are greater than the training data, the ELM may encounter the singularity problem, and its solution may become unstable. To overcome these limitations, several methods have been proposed within the regularization framework. In this article, we considered a greedy method for sparse approximation of the output weight vector of the ELM network. More specifically, the orthogonal matching pursuit (OMP) algorithm is embedded to the ELM. This new technique is named OMP-ELM. OMP-ELM has several advantages over regularized ELM methods, such as lower complexity and immunity to the singularity problem. Experimental works on nine commonly used regression problems indicate that the investigated OMP-ELM method confirms these advantages. Moreover, OMP-ELM is compared with the ELM method, the regularized ELM scheme, and artificial neural networks.

scholarly journals Palm Oil Prediction Production Using Extreme Learning Machine

2019 ◽  
Author(s):  
Ronal Watrianthos
Keyword(s):  
Extreme Learning Machine ◽  
Palm Oil ◽  
Oil Production ◽  
Training Data ◽  
Total Production ◽  
Land Area ◽  
Technical Data ◽  
Learning Machine ◽  
Hidden Neurons ◽  
Production Problems

The total production of Indonesian palm oil (CPO) in 2018 reached 43.9 million tons, with a land area of 12.3 million hectares.However, every month there are still many companies that have problems in predicting palm oil production. Problems in predicting thisproduction can be solved by calculation methods in the field of artificial neural networks, namely the Extreme Learning Machine (ELM)method. This method can solve linear and non-linear data problems and provide better average computation compared to other methods inpredicting oil palm production. The data used is palm oil production data at PT Indo Palm Oil Labuhan Batu with a total of 297 in the period2017-2018. While the parameters used are planting age, land area, number of trees, and yields. The results of the best-hidden neuron testare 13 with 2 technical data features and the training data pattern is pattern 1. The average MAPE value is 20.1% with the fastestcomputing time is the use of the number of hidden neurons 2. So based on the test results, the method ELM has a predictive model withquite good performance because the MAPE value is in the range of 20% -50%.

Review on mathematical models for the prediction of solar radiation

2019 ◽  
Vol 15 (1) ◽  
pp. 56
Author(s):  
Meenal Rajasekaran ◽  
A.Immanuel Selvakumar ◽  
E. Rajasekaran
Keyword(s):  
Solar Radiation ◽  
Mathematical Models ◽  
Global Solar Radiation ◽  
Measuring Instruments ◽  
Good Prediction ◽  
Energy Integration ◽  
Solar Pv ◽  
Pv System ◽  
Radiation Data ◽  
Good Prediction Accuracy

Global Solar Radiation (GSR) data is important for all solar energy based applications, mainly to forecast the output power of solar PV system in case of renewable energy integration in to the existing grid. The solar radiation components are measured using pyranometer, solarimeter, pyroheliometer and so on. It is not practically possible to install this radiation measuring instruments at all the locations due to the cost and difficulty in measurements. Hence the availability of solar radiation data is limited to few meteorological stations especially in the developing country like India. Therefore, it is necessary to develop mathematical models to predict the solar radiation to eliminate the costly pyranometer. In this paper, the review of mathematical models using trigonometric functions for the prediction of global solar radiation is presented. The mathematical models are applicable wherever the radiation data is unavailable. From the review results, it is concluded that mathematical model with both sine and cosine wave equation gives good prediction accuracy with correlation coefficient of 0.95

scholarly journals Sample Selected Extreme Learning Machine Based Intrusion Detection in Fog Computing and MEC

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Xingshuo An ◽  
Xianwei Zhou ◽  
Xing Lü ◽  
Fuhong Lin ◽  
Lei Yang
Keyword(s):  
Intrusion Detection ◽  
Extreme Learning Machine ◽  
Detection System ◽  
Fog Computing ◽  
Training Data ◽  
Experimental Simulation ◽  
Detection Accuracy ◽  
New Paradigm ◽  
Training Time ◽  
Learning Machine

Fog computing, as a new paradigm, has many characteristics that are different from cloud computing. Due to the resources being limited, fog nodes/MEC hosts are vulnerable to cyberattacks. Lightweight intrusion detection system (IDS) is a key technique to solve the problem. Because extreme learning machine (ELM) has the characteristics of fast training speed and good generalization ability, we present a new lightweight IDS called sample selected extreme learning machine (SS-ELM). The reason why we propose “sample selected extreme learning machine” is that fog nodes/MEC hosts do not have the ability to store extremely large amounts of training data sets. Accordingly, they are stored, computed, and sampled by the cloud servers. Then, the selected sample is given to the fog nodes/MEC hosts for training. This design can bring down the training time and increase the detection accuracy. Experimental simulation verifies that SS-ELM performs well in intrusion detection in terms of accuracy, training time, and the receiver operating characteristic (ROC) value.

Application of extreme learning machine for estimating solar radiation from satellite data

2013 ◽  
Vol 38 (2) ◽  
pp. 205-212 ◽  
Author(s):  
Mehmet Şahin ◽  
Yılmaz Kaya ◽  
Murat Uyar ◽  
Selçuk Yıldırım
Keyword(s):  
Solar Radiation ◽  
Extreme Learning Machine ◽  
Satellite Data ◽  
Learning Machine

A comparison of the performance of some extreme learning machine empirical models for predicting daily horizontal diffuse solar radiation in a region of southern Iran

2017 ◽  
Vol 38 (23) ◽  
pp. 6894-6909 ◽  
Author(s):  
Seyed Hossein Hosseini Nazhad ◽  
Mohammad Mehdi Lotfinejad ◽  
Malihe Danesh ◽  
Rooh ul Amin ◽  
Shahaboddin Shamshirband
Keyword(s):  
Solar Radiation ◽  
Extreme Learning Machine ◽  
Empirical Models ◽  
Diffuse Solar Radiation ◽  
Southern Iran ◽  
Learning Machine
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