Estimates of residential building energy consumption using a multi-verse optimizer-based support vector machine with k-fold cross-validation

2019 ◽  
Author(s):  
Hamed Tabrizchi ◽  
Mohammad Masoud Javidi ◽  
Vahid Amirzadeh
Keyword(s):  
Support Vector Machine ◽  
Energy Consumption ◽  
Cross Validation ◽  
Residential Building ◽  
Building Energy ◽  
Support Vector ◽  
Building Energy Consumption ◽  
Fold Cross Validation

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.

Combination of Support Vector Machine and K-Fold cross-validation for prediction of long-term degradation of the compressive strength of marine concrete

2018 ◽  
Vol 1 (1) ◽  
pp. 120-130 ◽  
Author(s):  
Chunxiang Qian ◽  
Wence Kang ◽  
Hao Ling ◽  
Hua Dong ◽  
Chengyao Liang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Environmental Factors ◽  
Cross Validation ◽  
Concrete Strength ◽  
Simulation Method ◽  
Support Vector ◽  
Svm Model ◽  
Artificial Neural Network Ann ◽  
Influence Degree ◽  
Fold Cross Validation

Support Vector Machine (SVM) model optimized by K-Fold cross-validation was built to predict and evaluate the degradation of concrete strength in a complicated marine environment. Meanwhile, several mathematical models, such as Artificial Neural Network (ANN) and Decision Tree (DT), were also built and compared with SVM to determine which one could make the most accurate predictions. The material factors and environmental factors that influence the results were considered. The materials factors mainly involved the original concrete strength, the amount of cement replaced by fly ash and slag. The environmental factors consisted of the concentration of Mg2+, SO42-, Cl-, temperature and exposing time. It was concluded from the prediction results that the optimized SVM model appeared to perform better than other models in predicting the concrete strength. Based on SVM model, a simulation method of variables limitation was used to determine the sensitivity of various factors and the influence degree of these factors on the degradation of concrete strength.

scholarly journals Pengenalan Wajah Manusia berbasis Algoritma Local Binary Pattern

2017 ◽  
Vol 17 (2) ◽  
pp. 29-38
Author(s):  
Ratih Purwati ◽  
Gunawan Ariyanto
Keyword(s):  
Computer Vision ◽  
Support Vector Machine ◽  
Face Recognition ◽  
Local Binary Pattern ◽  
Cross Validation ◽  
Support Vector ◽  
Fold Cross Validation

Face Recognition merupakan teknologi komputer untuk mengidentifikasi wajah manusia melalui gambar digital yang tersimpan di database. Wajah manusia dapat berubah bentuk sesuai dengan ekspresi yang dimilikinya. Wajah manusia dapat berubah bentuk sesuai dengan eskpresi yang dimilikinya. Ekspresi wajah manusia memiliki kemiripan satu sama lain sehingga untuk mengenali suatu ekspresi adalah kepunyaan siapa akan sedikit sulit. Pengenalan wajah terus menjadi topik aktif di zaman sekarang pada penelitian bidang computer vision. Penggunaan wajah manusia sering kita jumpai pada fitur-fitur aplikasi media sosial seperti Snapchat, Snapgram dari Instagram dan banyak aplikasi sosial media lainnya yang menggunakan teknologi tersebut. Pada penelitian ini dilakukan analisa pengenalan ekpresi wajah manusia dengan pendekatan fitur alogaritma Local Binary Pattern dan mencari pengembangan alogaritma dasar Local Binary Pattern yang paling optimal dengan cara menggabungkan metode Hisogram Equalization, Support Vector Machine, dan K-fold cross validation sehingga dapat meningkatkan pengenalan gambar wajah manusia pada hasil yang terbaik. Penelitian ini menginput beberapa database wajah manusia seperti JAFFE yang merupakan gambar wajah manusia wanita jepang yang berjumlah 10 orang dengan 7 ekspresi emosional seperti marah, sedih, bahagia, jijik, kaget, takut dan netral ke dalam sistem. YALE yaitu merupakan gambar wajah manusia orang Amerika. Serta menggunakan dataset CALTECH yang merupakan gambar manusia yang terdiri dari 450 gambar dengan ukuran 896 x 592 piksel dan disimpan dalam format JPEG. Kemudian data tersebut di sesuaikan dengan bentuk tekstur wajah masing-masing. Dari hasil penggabungan ketiga metode diatas dan percobaan-percobaan yang sudah dilakukan, didapatkan hasil yang paling optimal dalam pengenalan wajah manusia yaitu menggunakan dataset JAFFE dengan resolusi 92 x 112 piksel dan dengan tingkat penggunaan processor yang tinggi dapat mempengaruhi waktu kecepatan komputasi dalam proses menjalankan sistem sehingga menghasilkan prediksi yang lebih tepat.

scholarly journals Long-Term Prediction of Weather for Analysis of Residential Building Energy Consumption in Australia

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4805
Author(s):  
Shu Chen ◽  
Zhengen Ren ◽  
Zhi Tang ◽  
Xianrong Zhuo
Keyword(s):  
Climate Change ◽  
Energy Consumption ◽  
Residential Building ◽  
Building Energy ◽  
Space Heating ◽  
Building Energy Consumption ◽  
Climate Zones ◽  
Impact Of Climate Change ◽  
Heating And Cooling ◽  
The Impact

Globally, buildings account for nearly 40% of the total primary energy consumption and are responsible for 20% of the total greenhouse gas emissions. Energy consumption in buildings is increasing with the increasing world population and improving standards of living. Current global warming conditions will inevitably impact building energy consumption. To address this issue, this report conducted a comprehensive study of the impact of climate change on residential building energy consumption. Using the methodology of morphing, the weather files were constructed based on the typical meteorological year (TMY) data and predicted data generated from eight typical global climate models (GCMs) for three representative concentration pathways (RCP2.6, RCP4.5, and RCP8.5) from 2020 to 2100. It was found that the most severe situation would occur in scenario RCP8.5, where the increase in temperature will reach 4.5 °C in eastern Australia from 2080–2099, which is 1 °C higher than that in other climate zones. With the construction of predicted weather files in 83 climate zones all across Australia, ten climate zones (cities)—ranging from heating-dominated to cooling-dominated regions—were selected as representative climate zones to illustrate the impact of climate change on heating and cooling energy consumption. The quantitative change in the energy requirements for space heating and cooling, along with the star rating, was simulated for two representative detached houses using the AccuRate software. It could be concluded that the RCP scenarios significantly affect the energy loads, which is consistent with changes in the ambient temperature. The heating load decreases for all climate zones, while the cooling load increases. Most regions in Australia will increase their energy consumption due to rising temperatures; however, the energy requirements of Adelaide and Perth would not change significantly, where the space heating and cooling loads are balanced due to decreasing heating and increasing cooling costs in most scenarios. The energy load in bigger houses will change more than that in smaller houses. Furthermore, Brisbane is the most sensitive region in terms of relative space energy changes, and Townsville appears to be the most sensitive area in terms of star rating change in this study. The impact of climate change on space building energy consumption in different climate zones should be considered in future design strategies due to the decades-long lifespans of Australian residential houses.

Abstract 473: Identification of Apolipoproteins Using Feature Selection Technique

2016 ◽  
Vol 36 (suppl_1) ◽  
Author(s):  
Hua Tang ◽  
Hao Lin
Keyword(s):  
Support Vector Machine ◽  
Cross Validation ◽  
Support Vector ◽  
Feature Subset ◽  
Risk Markers ◽  
Dipeptide Composition ◽  
Accurate Identification ◽  
Feature Selection Technique ◽  
Physiological Importance ◽  
Fold Cross Validation

Objective: Apolipoproteins are of great physiological importance and are associated with different diseases such as dyslipidemia, thrombogenesis and angiocardiopathy. Apolipoproteins have therefore emerged as key risk markers and important research targets yet the types of apolipoproteins has not been fully elucidated. Accurate identification of the apoliproproteins is very crucial to the comprehension of cardiovascular diseases and drug design. The aim of this study is to develop a powerful model to precisely identify apolipoproteins. Approach and Results: We manually collected a non-redundant dataset of 53 apoliproproteins and 136 non-apoliproproteins with the sequence identify of less than 40% from UniProt. After formulating the protein sequence samples with g -gap dipeptide composition (here g =1~10), the analysis of various (ANOVA) was adopted to find out the best feature subset which can achieve the best accuracy. Support Vector Machine (SVM) was then used to perform classification. The predictive model was evaluated using a five-fold cross-validation which yielded a sensitivity of 96.2%, a specificity of 99.3%, and an accuracy of 98.4%. The study indicated that the proposed method could be a feasible means of conducting preliminary analyses of apoliproproteins. Conclusion: We demonstrated that apoliproproteins can be predicted from their primary sequences. Also we discovered the special dipeptide distribution in apoliproproteins. These findings open new perspectives to improve apoliproproteins prediction by considering the specific dipeptides. We expect that these findings will help to improve drug development in anti-angiocardiopathy disease. Key words: Apoliproproteins Angiocardiopathy Support Vector Machine

scholarly journals Applying support vector machines to predict building energy consumption in China

2018 ◽  
Vol 152 ◽  
pp. 780-786 ◽  
Author(s):  
Zhitong Ma ◽  
Cantao Ye ◽  
Huashan Li ◽  
Weibin Ma
Keyword(s):  
Energy Consumption ◽  
Support Vector Machines ◽  
Building Energy ◽  
Support Vector ◽  
Building Energy Consumption ◽  
Vector Machines

DWELLING FACTORS EFFECT ON RESIDENTIAL BUILDING ENERGY CONSUMPTION

2015 ◽  
Vol 77 (15) ◽  
Author(s):  
Jibrin Hassan Suleiman ◽  
Saeed Balubaid ◽  
Nasiru Mohammed Zakari ◽  
Egba Ernest Ituma
Keyword(s):  
Energy Consumption ◽  
Residential Buildings ◽  
Residential Building ◽  
Building Energy ◽  
Building Energy Consumption ◽  
Refrigeration System ◽  
Rapid Urbanization ◽  
Air Conditioning System ◽  
Household Heads ◽  
Self Administered Questionnaire

Most of the developing countries experience rapid urbanization and population growth, Malaysia is among these countries as the population and the energy consumption in the country tremendously increased over the last few decades.  A major challenge is the rate of energy consumption in the country is tremendous going higher which is a threat as the country was listed 26th out of the 30 top greenhouse emitters in the world.  A survey was conducted on the ways occupants’ consumes energy in their residential buildings in relation to dwelling factors in the State of Johor Malaysia. Energy consumption of the residential owners was assessed using drop and pick self-administered questionnaire. The questionnaires were answered by each household heads. Air conditioning system, refrigeration system, kitchen appliances, bathroom and laundry appliances, lighting appliances as well as other home appliances was considered in the survey. Correlation analysis was used using Statistical Package for Social Sciences (SPSS) to analyze the results. The finding shows a positive relationship between dwelling factors.  r ≥ 0.3 and above between dwelling factors and residential building energy consumption. 

scholarly journals Prediction of Residential Building Energy Consumption in Jilin Province, China

2006 ◽  
Vol 5 (2) ◽  
pp. 407-412 ◽  
Author(s):  
Xindong Wei ◽  
Ji Xuan ◽  
Jun Yin ◽  
Weijun Gao ◽  
Bill Batty ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Residential Building ◽  
Building Energy ◽  
Jilin Province ◽  
Building Energy Consumption
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