Prediction of building energy consumption using an improved real coded genetic algorithm based least squares support vector machine approach

2015 ◽  
Vol 90 ◽  
pp. 76-84 ◽  
Author(s):  
Hyun Chul Jung ◽  
Jin Sung Kim ◽  
Hoon Heo
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Energy Consumption ◽  
Least Squares ◽  
Building Energy ◽  
Support Vector ◽  
Building Energy Consumption ◽  
Real Coded Genetic Algorithm

scholarly journals Prediksi Konsumsi Energi Pada Bangunan Menggunakan Metode Support Vector Machice Berbasis Algoritma Genetika

2017 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
Wida Prima Mustika
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Energy Consumption ◽  
Support Vector Machines ◽  
Energy Use ◽  
Building Energy ◽  
Support Vector ◽  
Vector Machines ◽  
Optimization Parameters

Energy consumption is a demand for the amount of energy that must supply the building at any given time. Building energy consumption has continued increased over the last few decades all over the world, and Heating, Ventilating, and Air-Conditioning (HVAC), which has a catalytic role in regulating the temperature in the room, mostly accounted for of building energy use. Models created for in this study support vector machine and support vector machine-based models of genetic algorithm to obtain the value of accuracy or error rate or the smallest error value Root Mean Square Error (RMSE) in predicting energy consumption in buildings is more accurate. After testing the two models of support vector machines and support vector machines based on the genetic algorithm is the testing results obtained by using support vector machines where RMSE value obtained was 2,613. Next was the application of genetic algorithms to the optimization parameters C and γ values obtained RMSE error of 1.825 and a genetic algorithm for feature selection error RMSE values obtained for 1,767 of the 7 predictor variables and the selection attribute or feature resulting in the election of three attributes used. After that is done the optimization parameters and the importance of the value of feature selection mistake or error of the smallest RMSE of 1.537. Thus the support vector machine algorithm based on genetic algorithm can give a solution to the problems in the prediction of energy consumption rated the smallest mistake or error.

scholarly journals PREDICTION OF GOVERNMENT-OWNED BUILDING ENERGY CONSUMPTION BASED ON AN RRELIEFF AND SUPPORT VECTOR MACHINE MODEL

2015 ◽  
Vol 21 (6) ◽  
pp. 748-760 ◽  
Author(s):  
Hyojoo Son ◽  
Changmin Kim ◽  
Changwan Kim ◽  
Youngcheol Kang
Keyword(s):  
Support Vector Machine ◽  
Energy Consumption ◽  
Environmental Impact ◽  
Building Energy ◽  
Support Vector ◽  
Government Agencies ◽  
Design Phase ◽  
Building Energy Consumption ◽  
Machine Model ◽  
Proposed Model

Accurate prediction of the energy consumption of government-owned buildings in the design phase is vital for government agencies, as it enables formulation of the early phases of development of such buildings with a view to reducing their environmental impact. The aim of this study was to identify the variables that are associated with energy consumption in government-owned buildings and to propose a predictive model based on those variables. The proposed approach selects relevant variables using the RReliefF variable selection algorithm. The support vector machine (SVM) method is used to develop a model of energy consumption based on the identified variables. The proposed approach was analyzed and validated on data for 175 government-owned buildings derived from the 2003 Commercial Building Energy Consumption Survey (CBECS) database. The experimental results revealed that the proposed model is able to predict the energy consumption of government-owned buildings in the design phase with a reasonable level of accuracy. The proposed model could be beneficial in guiding government agencies in developing early strategies and proactively reducing the environmental impact of a building, thereby achieving a high degree of sustainability of buildings constructed for government agencies.

Sensor Dynamic Modeling Based on LS-SVM and NGA

2008 ◽  
Vol 381-382 ◽  
pp. 439-442
Author(s):  
Qi Wang ◽  
Zhi Gang Feng ◽  
K. Shida
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Support Vector Machine ◽  
Least Squares ◽  
Dynamic Model ◽  
Dynamic Modeling ◽  
Support Vector ◽  
Local Minima ◽  
Highly Nonlinear ◽  
Sharing Function

Least squares support vector machine (LS-SVM) combined with niche genetic algorithm (NGA) are proposed for nonlinear sensor dynamic modeling. Compared with neural networks, the LS-SVM can overcome the shortcomings of local minima and over fitting, and has higher generalization performance. The sharing function based niche genetic algorithm is used to select the LS-SVM parameters automatically. The effectiveness and reliability of this method are demonstrated in two examples. The results show that this approach can escape from the blindness of man-made choice of LS-SVM parameters. It is still effective even if the sensor dynamic model is highly nonlinear.

OPTIMIZATION OF LEAST SQUARES SUPPORT VECTOR MACHINE TECHNIQUE USING GENETIC ALGORITHM FOR ELECTROENCEPHALOGRAM MULTI-DIMENSIONAL SIGNALS

2016 ◽  
Vol 78 (5-10) ◽  
Author(s):  
Farzana Kabir Ahmad ◽  
Abdullah Yousef Awwad Al-Qammaz ◽  
Yuhanis Yusof
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Computational Model ◽  
Least Squares ◽  
Support Vector ◽  
Feature Selection Technique ◽  
Selection Technique ◽  
Human Emotion ◽  
Valence And Arousal

Human-computer intelligent interaction (HCII) is a rising field of science that aims to refine and enhance the interaction between computer and human. Since emotion plays a vital role in human daily life, the ability of computer to interpret and response to human emotion is a crucial element for future intelligent system. Accordingly, several studies have been conducted to recognise human emotion using different technique such as facial expression, speech, galvanic skin response (GSR), or heart rate (HR). However, such techniques have problems mainly in terms of credibility and reliability as people can fake their feeling and response. Electroencephalogram (EEG) on the other has shown to be a very effective way in recognising human emotion as this technique records the brain activity of human and they can hardly be deceived by voluntary control. Regardless the popularity of EEG in recognizing human emotion, this study field is relatively challenging as EEG signal is nonlinear, involves myriad factors and chaotic in nature. These issues have led to high dimensional problem and poor classification results. To address such problems, this study has proposed a novel computational model, which consist of three main stages, namely a) feature extraction; b) feature selection and c) classifier. Discrete wavelet packet transform (DWPT) has been used to extract EEG signals feature and ultimately 204,800 features from 32 subject-independent have been obtained. Meanwhile, Genetic Algorithm (GA) and Least squares support vector machine (LS-SVM) have been used as a feature selection technique and classifier respectively. This computational model is tested on the common DEAP pre-processed EEG dataset in order to classify three levels of valence and arousal. The empirical results have shown that the proposed GA-LSSVM, has improved the classification results to 49.22% and 54.83% for valence and arousal respectively, whereas is it observed that 46.33% of valence and 48.30% of arousal classification were achieved when no feature selection technique is applied on the identical classifier

THE USE OF LS-SVM FOR SHORT-TERM PASSENGER FLOW PREDICTION / MAŽIAUSIŲ KVADRATŲ ATRAMINIŲ VEKTORIŲ METODO TAIKYMAS TRUMPALAIKIAM KELEIVIŲ SRAUTUI PROGNOZUOTI / ПРИМЕНЕНИЕ МЕТОДА ОПОРНЫХ ВЕКТОРОВ С КВАДРАТИЧНОЙ ФУНКЦИЕЙ ПОТЕРЬ ДЛЯ ПРОГНОЗИРОВАНИЯ КРАТКОСРОЧНЫХ ПОТОКОВ ПАССАЖИРОВ

Transport ◽  
2011 ◽  
Vol 26 (1) ◽  
pp. 5-10 ◽  
Author(s):  
Qian Chen ◽  
Wenquan Li ◽  
Jinhuan Zhao
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Prediction Model ◽  
Least Squares ◽  
Support Vector ◽  
Short Term ◽  
Passenger Flow ◽  
Flow Prediction ◽  
Transit Flow ◽  
Kernel Parameter

Transit flow is the basement of transit planning and scheduling. The paper presents a new transit flow prediction model based on Least Squares Support Vector Machine (LS-SVM). With reference to the theory of Support Vector Machine and Genetic Algorithm, a new short-term passenger flow prediction model is built employing LSSVM, and a new evaluation indicator is used for presenting training permanence. An improved genetic algorithm is designed by enhancing crossover and variation in the use of optimizing the penalty parameter γ and kernel parameter s in LS-SVM. By using this method, passenger flow in a certain bus route is predicted in Changchun. The obtained result shows that there is little difference between actual value and prediction, and the majority of the equal coefficients of a training set are larger than 0.90, which shows the validity of the approach. Santrauka Tranzito srautas yra tranzito planavimo ir eismo tvarkaraščių sudarymo pagrindas. Straipsnis pateikia naują tranzitinio srauto prognozavimo modelį, grindžiamą mažiausių kvadratų atraminių vektorių metodu (Least Squares Support Vector machine, LS-SVm). Remiantis atraminių vektorių metodu (Support Vector machine) ir genetiniu algoritmu (Genetic Algorithm), sudarytas naujas trumpalaikis keleivių srauto prognozavimo modelis, pasitelkiant LS-SVM ir pristatomas naujas vertinimo rodiklis. Taikant naują metodą prognozuojamas keleivių srautas konkrečiame autobuso maršrute Čangčuno mieste Kinijoje. Gautos prognozės rezultatai lyginami su faktiniais. Резюме Транзитный поток – основной фактор при планировании транзита и составлении расписаний движения. В статье представлена новая модель прогноз*а транзитного потока, основанная на методе опорных векторов с квадратичной функцией потерь (Least Squares Support Vector machine – LS-SVm). Представленный новый метод используется для прогноза потока пассажиров на конкретном автобусном маршруте города Чаньчуня (Китай). Результаты прогноза сравниваются с фактическими результатами.

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