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.

scholarly journals Using Enhanced Sparrow Search Algorithm-Deep Extreme Learning Machine Model to Forecast End-Point Phosphorus Content of BOF

2021 ◽  
Author(s):  
Lingxiang Quan ◽  
Ailian Li ◽  
Guimei Cui ◽  
Shaofeng Xie
Keyword(s):  
Extreme Learning Machine ◽  
Prediction Accuracy ◽  
Phosphorus Content ◽  
Prediction Models ◽  
Search Algorithm ◽  
Process Data ◽  
Machine Model ◽  
End Point ◽  
Cauchy Mutation ◽  
Learning Machine

：An effective technology for predicting the end-point phosphorous content of basic oxygen furnace (BOF) can provide theoretical instruction to improve the quality of steel via controlling the hardness and toughness. Given the slightly inadequate prediction accuracy in the existing prediction model, a novel hybrid method was suggested to more accurately predict the end-point phosphorus content by integrating an enhanced sparrow search algorithm (ESSA) and a multi-strategy with a deep extreme learning machine (DELM) as ESSA-DELM in this study. To begin with, the input weights and hidden biases of DELM were randomly selected, resulting in that DELM inevitably had a set of non-optimal or unnecessary weights and biases. Therefore, the ESSA was used to optimize the DELM in this work. For the ESSA, the Trigonometric substitution mechanism and Cauchy mutation were introduced to avoid trapping in local optima and improve the global exploration capacity in SSA. Finally, to evaluate the prediction efficiency of ESSSA-DELM, the proposed model was tested on process data of the converter from the Baogang steel plant. The efficacy of ESSA-DELM was more superior to that of other DELM-based hybrid prediction models and conventional models. The result demonstrated that the hit rate of end-point phosphorus content within ±0.003%, ±0.002%, and ±0.001% was 91.67%, 83.33%, and 63.55%, respectively. The proposed ESSA-DELM model could possess better prediction accuracy compared with other models, which could guide field operations.

scholarly journals Occupancy Prediction Using Differential Evolution Online Sequential Extreme Learning Machine Model

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 4033
Author(s):  
Jonas Bielskus ◽  
Violeta Motuzienė ◽  
Tatjana Vilutienė ◽  
Audrius Indriulionis
Keyword(s):  
Energy Efficiency ◽  
Differential Evolution ◽  
Extreme Learning Machine ◽  
Open Space ◽  
Prediction Models ◽  
Co2 Concentration ◽  
Full Potential ◽  
Machine Model ◽  
Learning Machine ◽  
Occupancy Prediction

Despite increasing energy efficiency requirements, the full potential of energy efficiency is still unlocked; many buildings in the EU tend to consume more energy than predicted. Gathering data and developing models to predict occupants’ behaviour is seen as the next frontier in sustainable design. Measurements in the analysed open-space office showed accordingly 3.5 and 2.7 times lower occupancy compared to the ones given by DesignBuilder’s and EN 16798-1. This proves that proposed occupancy patterns are only suitable for typical open-space offices. The results of the previous studies and proposed occupancy prediction models have limited applications and limited accuracies. In this paper, the hybrid differential evolution online sequential extreme learning machine (DE-OSELM) model was applied for building occupants’ presence prediction in open-space office. The model was not previously applied in this area of research. It was found that prediction using experimentally gained indoor and outdoor parameters for the whole analysed period resulted in a correlation coefficient R2 = 0.72. The best correlation was found with indoor CO2 concentration—R2 = 0.71 for the analysed period. It was concluded that a 4 week measurement period was sufficient for the prediction of the building’s occupancy and that DE-OSELM is a fast and reliable model suitable for this purpose.

scholarly journals Settlement Prediction of Foundation Pit Excavation Based on the GWO-ELM Model considering Different States of Influence

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Qiao Shi-fan ◽  
Tan Jun-kun ◽  
Zhang Yong-gang ◽  
Wan Li-jun ◽  
Zhang Ming-fei ◽  
...  
Keyword(s):  
Time Series ◽  
Extreme Learning Machine ◽  
Absolute Error ◽  
Ground Subsidence ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Foundation Pit ◽  
Machine Model ◽  
Model Based ◽  
Learning Machine

This paper proposes a novel grey wolf optimization-extreme learning machine model, namely, the GWO-ELM model, to train and predict the ground subsidence by combining the extreme learning machine with the grey wolf optimization algorithm. Taking an excavation project of a foundation pit of Kunming in China as an example, after analyzing the settlement monitoring data of cross sections JC55 and JC56, the representative monitoring sites JC55-2 and JC56-1 were selected as the training monitoring samples of the GWO-ELM model. And three kinds of GWO-ELM models such as considering the influence of time series, influence of settlement factors, and after optimization were established to predict the ground settlement near the foundation pit. The predictive results are that their average relative error and average absolute error are ranked from large to small as GWO-ELM model based on time series, GWO-ELM model based on settlement factors, and optimized GWO-ELM model for the three kinds of GWO-ELM models at monitoring points JC55-2 and JC56-1. Accordingly, the optimized GWO-ELM model has the strongest predictive ability.

Daily global solar radiation prediction based on a hybrid Coral Reefs Optimization – Extreme Learning Machine approach

Solar Energy ◽  
2014 ◽  
Vol 105 ◽  
pp. 91-98 ◽  
Author(s):  
S. Salcedo-Sanz ◽  
C. Casanova-Mateo ◽  
A. Pastor-Sánchez ◽  
M. Sánchez-Girón
Keyword(s):  
Solar Radiation ◽  
Coral Reefs ◽  
Extreme Learning Machine ◽  
Global Solar Radiation ◽  
Learning Machine

scholarly journals Global Solar Radiation Estimation and Climatic Variability Analysis Using Extreme Learning Machine Based Predictive Model

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 12026-12042 ◽  
Author(s):  
Tao Hai ◽  
Ahmad Sharafati ◽  
Achite Mohammed ◽  
Sinan Q. Salih ◽  
Ravinesh C. Deo ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Predictive Model ◽  
Extreme Learning Machine ◽  
Climatic Variability ◽  
Global Solar Radiation ◽  
Variability Analysis ◽  
Learning Machine

A comparative evaluation for identifying the suitability of extreme learning machine to predict horizontal global solar radiation

2015 ◽  
Vol 52 ◽  
pp. 1031-1042 ◽  
Author(s):  
Shahaboddin Shamshirband ◽  
Kasra Mohammadi ◽  
Por Lip Yee ◽  
Dalibor Petković ◽  
Ali Mostafaeipour
Keyword(s):  
Solar Radiation ◽  
Extreme Learning Machine ◽  
Comparative Evaluation ◽  
Global Solar Radiation ◽  
Learning Machine

Daily global solar radiation prediction from air temperatures using kernel extreme learning machine: A case study for Iran

2015 ◽  
Vol 134 ◽  
pp. 109-117 ◽  
Author(s):  
Shahaboddin Shamshirband ◽  
Kasra Mohammadi ◽  
Hui-Ling Chen ◽  
Ganthan Narayana Samy ◽  
Dalibor Petković ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Extreme Learning Machine ◽  
Global Solar Radiation ◽  
Air Temperatures ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

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 Fault Tolerance Automotive Air-Ratio Control Using Extreme Learning Machine Model Predictive Controller

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Pak Kin Wong ◽  
Hang Cheong Wong ◽  
Chi Man Vong ◽  
Tong Meng Iong ◽  
Ka In Wong ◽  
...  
Keyword(s):  
Fault Tolerance ◽  
Extreme Learning Machine ◽  
External Disturbance ◽  
Feedback Signal ◽  
Ratio Model ◽  
Control Performance ◽  
Machine Model ◽  
Target Values ◽  
Lambda Sensor ◽  
Learning Machine

Effective air-ratio control is desirable to maintain the best engine performance. However, traditional air-ratio control assumes the lambda sensor located at the tail pipe works properly and relies strongly on the air-ratio feedback signal measured by the lambda sensor. When the sensor is warming up during cold start or under failure, the traditional air-ratio control no longer works. To address this issue, this paper utilizes an advanced modelling technique, kernel extreme learning machine (ELM), to build a backup air-ratio model. With the prediction from the model, a limited air-ratio control performance can be maintained even when the lambda sensor does not work. Such strategy is realized as fault tolerance control. In order to verify the effectiveness of the proposed fault tolerance air-ratio control strategy, a model predictive control scheme is constructed based on the kernel ELM backup air-ratio model and implemented on a real engine. Experimental results show that the proposed controller can regulate the air-ratio to specific target values within a satisfactory tolerance under external disturbance and the absence of air-ratio feedback signal from the lambda sensor. This implies that the proposed fault tolerance air-ratio control is a promising scheme to maintain air-ratio control performance when the lambda sensor is under failure or warming up.

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