Prediction of energy performance of residential buildings: A genetic programming approach

Koklu

et al. 2017

ijisae

Abstract:The definition of the data mining can be told as to extract information or knowledge from large volumes of data. Statistical and machine learning techniques are used for the determination of the models to be used for data mining predictions. Today, data mining is used in many different areas such as science and engineering, health, commerce, shopping, banking and finance, education and internet. This study make use of WEKA (Waikato Environment for Knowledge Analysis) to compare the different classification techniques on energy efficiency datasets. In this study 10 different Data Mining methods namely Bagging, Decorate, Rotation Forest, J48, NNge, K-Star, Naïve Bayes, Dagging, Bayes Net and JRip classification methods were applied on energy efficiency dataset that were taken from UCI Machine Learning Repository. When comparing the performances of algorithms it's been found that Rotation Forest has highest accuracy whereas Dagging had the worst accuracy.

Section: Literature Surveymentioning

confidence: 80%

Section: Introductionmentioning

confidence: 99%

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Comparison of Classification Techniques on Energy Efficiency Dataset

Koklu

et al. 2017

ijisae

“…Apart from machine-learning, genetic algorithms are also a promising method of analysing building energy performance. Castelli et al [22] developed a model using genetic programming approach with geometric semantic genetic programming (GSGP). The model predicted both heating and cooling load of a set of residential buildings.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Short-Term Prediction of Air-Conditioning Load through Smart Meter Analytics

2017

Abstract:The present paper is focused on short-term prediction of air-conditioning (AC) load of residential buildings using the data obtained from a conventional smart meter. The AC load, at each time step, is separated from smart meter's aggregate consumption through energy disaggregation methodology. The obtained air-conditioning load and the corresponding historical weather data are then employed as input features for the prediction procedure. In the prediction step, different machine learning algorithms, including Artificial Neural Networks, Support Vector Machines, and Random Forests, are used in order to conduct hour-ahead and day-ahead predictions. The predictions obtained using Random Forests have been demonstrated to be the most accurate ones leading to hour-ahead and day-ahead prediction with R 2 scores of 87.3% and 83.2%, respectively. The main advantage of the present methodology is separating the AC consumption from the consumptions of other residential appliances, which can then be predicted employing short-term weather forecasts. The other devices' consumptions are largely dependent upon the occupant's behaviour and are thus more difficult to predict. Therefore, the harsh alterations in the consumption of AC equipment, due to variations in the weather conditions, can be predicted with a higher accuracy; which in turn enhances the overall load prediction accuracy.

“…The fast and mostly uncontrolled population growth create a pattern of rapid urbanization (Global Report on Human Settlements, 2001). Improved living standards and the increased population in developing countries contribute to a dramatic increase in energy consumption worldwide [4]. An increase in the urban population of 1% has been reported to increase energy use by 2.2% [5].…”

Section: Introductionmentioning

confidence: 99%

Effect of landscape density in a residential area on thermal performance in a tropical climate

Kamarulzaman

Zawawi²,

Mohammed³

2016

MATEC Web Conf.

Abstract. It is a well-documented that the urban air temperature is gradually growing in all cities due to the rapid development of buildings, roads and other infrastructure, human activities and also decreasing in vegetated areas. In a tropical climate, outdoor environment is clearly warmer than indoor environment due to higher air temperatures, particularly in dry seasons. Since the indoor environment is influenced by its surroundings, this situation indirectly contributes to the discomfort indoor environment in the building. Thus, it generates to the dependence on mechanical ventilation and increase the energy consumption in buildings. Many research studies have proof that plants not only beauty a city, but also improve the urban environmental condition by reducing the transferring of heat flux on buildings and increasing the reflection of radiation and shading. Therefore strategically placed vegetation around a building could decrease the energy consumption in buildings by reducing the adverse impact of some climate elements. Overall, this paper focuses on the results of a preliminary pilot study of two Semi-Detached houses with different landscape density in Seri Iskandar, Perak. Three climatic parameters, building configuration, and landscape design measured and analyze in this paper.