Robust total energy demand estimation with a hybrid Variable Neighborhood Search – Extreme Learning Machine algorithm

2016 ◽  
Vol 123 ◽  
pp. 445-452 ◽  
Author(s):  
J. Sánchez-Oro ◽  
A. Duarte ◽  
S. Salcedo-Sanz
Keyword(s):  
Total Energy ◽  
Extreme Learning Machine ◽  
Energy Demand ◽  
Variable Neighborhood Search ◽  
Demand Estimation ◽  
Neighborhood Search ◽  
Hybrid Variable ◽  
Learning Machine

scholarly journals A hybrid variable neighborhood search algorithm for targeted offers in direct marketing

2015 ◽  
Vol 47 ◽  
pp. 205-212 ◽  
Author(s):  
T.A. Oliveira ◽  
V.N. Coelho ◽  
M.J.F. Souza ◽  
D.L.T. Boava ◽  
F. Boava ◽  
...  
Keyword(s):  
Variable Neighborhood Search ◽  
Search Algorithm ◽  
Direct Marketing ◽  
Neighborhood Search ◽  
Hybrid Variable ◽  
Neighborhood Search Algorithm

Total Energy Demand Estimation in Iran Using Bees Algorithm

2011 ◽  
Vol 6 (3) ◽  
pp. 294-303 ◽  
Author(s):  
M. A. Behrang ◽  
E. Assareh ◽  
M. R. Assari ◽  
A. Ghanbarzadeh
Keyword(s):  
Total Energy ◽  
Energy Demand ◽  
Demand Estimation ◽  
Bees Algorithm

scholarly journals Office Building’s Occupancy Prediction Using Extreme Learning Machine Model with Different Optimization Algorithms

2021 ◽  
Vol 25 (1) ◽  
pp. 525-536
Author(s):  
Violeta Motuzienė ◽  
Jonas Bielskus ◽  
Vilūnė Lapinskienė ◽  
Genrika Rynkun
Keyword(s):  
Extreme Learning Machine ◽  
Energy Demand ◽  
Energy Performance ◽  
Machine Model ◽  
Size Number ◽  
Building Management ◽  
Use Phase ◽  
Learning Machine ◽  
Occupancy Prediction ◽  
Accuracy Of Prediction

Abstract Increasing energy efficiency requirements lead to lower energy consumption in buildings, but at the same time occupants’ influence on the energy balance of the building during the use phase becomes more crucial. The randomness of the building’s occupancy often leads to the mismatch of the predicted and measured energy demand, also called Energy Performance Gap. Therefore, prediction of occupancy is important both in the design and use phases of the building. The goal of the study is to apply Extreme Learning Machine (ELM) models with different optimisation algorithms – Genetic (GA-ELM) and Simulated Annealing (SA–ELM) for occupancy prediction in an office building based on measured CO2 concentrations. Both models show similar and high accuracy of prediction: R2 – 0.73–0.74 and RMSE – 1.8–1.9 for the whole measured period. Influence of population size, number of neurons, and number of iterations on results accuracy was also analysed and recommendations are given. It was concluded that both methods are suitable for occupancy prediction, but because of different simulation times, SA-ELM is recommended for the Building Management Systems (BMS), where higher speed is required.

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