scholarly journals Prediction of Heating and Cooling Load to improve Energy Efficiency of Buildings Using Machine Learning Techniques

2018 ◽  
Vol 13 (5) ◽  
Author(s):  
Srihari J
Keyword(s):  
Machine Learning ◽  
Energy Efficiency ◽  
Machine Learning Techniques ◽  
Cooling Load ◽  
Improve Energy Efficiency ◽  
Heating And Cooling ◽  
Learning Techniques ◽  
Heating And Cooling Load

scholarly journals Prediction of Building Energy Consumption Based on IPSO-CLSTM Neural Network

2021 ◽  
Vol 3 (2) ◽  
pp. 11
Author(s):  
Qingwu Fan ◽  
Li Shuo ◽  
Xudong Liu
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Short Term Memory ◽  
Design Stage ◽  
Cooling Load ◽  
Improve Energy Efficiency ◽  
Heating And Cooling ◽  
Heating And Cooling Load ◽  
Ipso Algorithm ◽  
The Impact

Accurate prediction of building load is essential for energy saving and environmental protection. Exploring the impact of building characteristics on heating and cooling load can improve energy efficiency from the design stage of the building. In this paper, a prediction model of building heating and cooling loads is proposed, which based on Improved Particle Swarm Optimization (IPSO) algorithm and Convolution Long Short-Term Memory (CLSTM) neural network model. Firstly, the characteristic variables are extracted and evaluated by Spearman’s correlation coefficient method; Then the prediction model based on the CLSTM neural network is constructed to predict building heating and cooling load. The IPSO algorithm is adopted to solve the problem that manual work cannot precisely adjust parameters. In this method, the optimization ability of the PSO algorithm is improved by changing the updating rule of inertia weight and learning factors. Finally, the parameters of the neural network are taken as IPSO optimization object to improve the prediction accuracy. In the experimental stage of this paper, a variety of algorithm models are compared, and the results show that IPSO-CLSTM can get the best results in the prediction of heating and cooling load.

scholarly journals Performance Evaluation of Two Machine Learning Techniques in Heating and Cooling Loads Forecasting of Residential Buildings

2020 ◽  
Vol 10 (11) ◽  
pp. 3829 ◽  
Author(s):  
Arash Moradzadeh ◽  
Amin Mansour-Saatloo ◽  
Behnam Mohammadi-Ivatloo ◽  
Amjad Anvari-Moghaddam
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Residential Buildings ◽  
Load Forecasting ◽  
Training Data ◽  
Support Vector ◽  
Cooling Load ◽  
Heating And Cooling ◽  
Cooling Loads ◽  
Heating And Cooling Load

Nowadays, since energy management of buildings contributes to the operation cost, many efforts are made to optimize the energy consumption of buildings. In addition, the most consumed energy in the buildings is assigned to the indoor heating and cooling comforts. In this regard, this paper proposes a heating and cooling load forecasting methodology, which by taking this methodology into the account energy consumption of the buildings can be optimized. Multilayer perceptron (MLP) and support vector regression (SVR) for the heating and cooling load forecasting of residential buildings are employed. MLP and SVR are the applications of artificial neural networks and machine learning, respectively. These methods commonly are used for modeling and regression and produce a linear mapping between input and output variables. Proposed methods are taught using training data pertaining to the characteristics of each sample in the dataset. To apply the proposed methods, a simulated dataset will be used, in which the technical parameters of the building are used as input variables and heating and cooling loads are selected as output variables for each network. Finally, the simulation and numerical results illustrates the effectiveness of the proposed methodologies.

scholarly journals Buildings Energy Efficiency Analysis and Classification Using Various Machine Learning Technique Classifiers

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3497 ◽  
Author(s):  
César Benavente-Peces ◽  
Nisrine Ibadah
Keyword(s):  
Machine Learning ◽  
Energy Efficiency ◽  
Data Science ◽  
Smart Cities ◽  
Thermal Modeling ◽  
Modern Society ◽  
Machine Learning Techniques ◽  
Single Family ◽  
Residential Areas ◽  
Learning Techniques

Energy efficiency is a major concern to achieve sustainability in modern society. Smart cities sustainability depends on the availability of energy-efficient infrastructures and services. Buildings compose most of the city, and they are responsible for most of the energy consumption and emissions to the atmosphere (40%). Smart cities need smart buildings to achieve sustainability goals. Building’s thermal modeling is essential to face the energy efficiency race. In this paper, we show how ICT and data science technologies and techniques can be applied to evaluate the energy efficiency of buildings. In concrete, we apply machine learning techniques to classify buildings based on their energy efficiency. Particularly, our focus is on single-family buildings in residential areas. Along this paper, we demonstrate the capabilities of machine learning techniques to classify buildings depending on their energy efficiency. Moreover, we analyze and compare the performance of different classifiers. Furthermore, we introduce new parameters which have some impact on the buildings thermal modeling, especially those concerning the environment where the building is located. We also make an insight on ICT and remark the growing relevance in data acquisition and monitoring of relevant parameters by using wireless sensor networks. It is worthy to remark the need for an appropriate and reliable dataset to achieve the best results. Moreover, we demonstrate that reliable classification is feasible with a few featured parameters.

Machine Learning Techniques for Energy Efficiency Prediction: A Comparative Studies

2019 ◽  
Vol 16 (5) ◽  
pp. 1783-1793
Author(s):  
Jamal A Alhiyafi ◽  
Aisha Alfuraih ◽  
Mai Alismail ◽  
Rawan Aljabr ◽  
Reem Alabdulazeem ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Efficiency ◽  
Comparative Studies ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals Thermal Performance of Dwellings with Rooftop PV Panels and PV/Thermal Collectors

Energies ◽  
2018 ◽  
Vol 11 (7) ◽  
pp. 1879 ◽  
Author(s):  
Saad Odeh
Keyword(s):  
Energy Efficiency ◽  
Thermal Resistance ◽  
Thermal Performance ◽  
Building Design ◽  
Cooling Load ◽  
Solar Pv ◽  
Pv System ◽  
Total Thermal Resistance ◽  
Heating And Cooling ◽  
Heating And Cooling Load

To improve the energy efficiency of dwellings, rooftop photovoltaic (PV) technology is proposed in contemporary designs; however, adopting this technology will add a new component to the roof that may affect its thermal balance. This paper studies the effect of roof shading developed by solar PV panels on dwellings’ thermal performance. The analysis in this work is performed by using two types of software packages: “AccuRate Sustainability” for rating the energy efficiency of a residential building design, and “PVSYST” for the solar PV power system design. AccuRate Sustainability is used to calculate the annual heating and cooling load, and PVSYST is used to evaluate the power production from the rooftop PV system. The analysis correlates the electrical energy generated from the PV panels to the change in the heating and cooling load due to roof shading. Different roof orientations, roof inclinations, and roof insulation, as well as PV dwelling floor areas, are considered in this study. The analysis shows that the drop in energy efficiency due to the shaded area of the roof by PV panels is very small compared to the energy generated by these panels. The analysis also shows that, with an increasing number of floors in the dwelling, the effect of shading by PV panels on thermal performance becomes negligible. The results show that insensitivity of the annual heating and cooling load to the thermal resistance of rooftop solar systems is only because the total thermal resistance is dominated by roof insulation.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques
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