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2022 | OriginalPaper | Chapter

3. Building Energy Management

Authors : Nor Azuana Ramli, Mel Keytingan M. Shapi

Published in: Control of Smart Buildings

Publisher: Springer Nature Singapore

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Abstract

Statistics show that approximate energy usage in a building is 10–20 times more than residential which is around 70–300 kWh/m2. The electricity demand is expected to increase triple than current demand in 2030. It is found that total energy demand and produced are not balanced whereby there will be not enough energy to supply for higher demand in the future. This why we need to manage energy properly especially for commercial building. Thanks to technology, now there is no need for building owners to hire energy auditor in order to know how to manage energy in their building. Technology has evolved commercial building into smart building. By installing sensors in the building and make use of Internet of Things technology, the energy can be managed through web or mobile apps. In this chapter, we are going to explain on how building evolved from commercial building to smart building and the development of building energy management by using machine learning and big data analytic approach.

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Literature
3.
go back to reference Mazlan NL, Ramli NA, Awalin LJ, Ismail MB, Kassim A, Menon A (2020) A smart building energy management using Internet of Things (IoT) and machine learning. Test Eng Manag 83:8083–8090 Mazlan NL, Ramli NA, Awalin LJ, Ismail MB, Kassim A, Menon A (2020) A smart building energy management using Internet of Things (IoT) and machine learning. Test Eng Manag 83:8083–8090
4.
go back to reference Shapi MKM, Ramli NA, Awalin LJ (2021).Energy consumption prediction by using machine learning for smart building: case study in Malaysia. Dev Built Environ 5 Shapi MKM, Ramli NA, Awalin LJ (2021).Energy consumption prediction by using machine learning for smart building: case study in Malaysia. Dev Built Environ 5
5.
go back to reference Ahmad AS, Hassan MY, Abdullah H, Rahman HA, Majid MS, Bandi M (2012) Energy efficiency measurements in a Malaysian public university. In: 2012 IEEE international conference on power and energy (PECon). Kota Kinabalu, Malaysia Ahmad AS, Hassan MY, Abdullah H, Rahman HA, Majid MS, Bandi M (2012) Energy efficiency measurements in a Malaysian public university. In: 2012 IEEE international conference on power and energy (PECon). Kota Kinabalu, Malaysia
6.
go back to reference Akkaya K, Guvenc I, Aygun R, Pala N, Kadri A (2015) IoT-based occupancy monitoring techniques for energy-efficient smart buildings. IEEE Wirel Commun Netw Conf Work Akkaya K, Guvenc I, Aygun R, Pala N, Kadri A (2015) IoT-based occupancy monitoring techniques for energy-efficient smart buildings. IEEE Wirel Commun Netw Conf Work
7.
go back to reference Al-Ali AR, Zualkernan IA, Rashid M, Gupta R, Alikarar M (2017) A smart home energy management system using IoT and big data analytics approach. IEEE Trans Consum Electron Al-Ali AR, Zualkernan IA, Rashid M, Gupta R, Alikarar M (2017) A smart home energy management system using IoT and big data analytics approach. IEEE Trans Consum Electron
8.
go back to reference Kaytez F, Taplamacioglu M, Ertugul C, Hardalac F (2015) Forecasting electricity consumption: a comparison of regression analysis, neural network and lest squares support vector machines. Int J Electr Power Energy Syst. https://doi.org/67.10.1016/j.ipes.2014.12.036 Kaytez F, Taplamacioglu M, Ertugul C, Hardalac F (2015) Forecasting electricity consumption: a comparison of regression analysis, neural network and lest squares support vector machines. Int J Electr Power Energy Syst. https://​doi.​org/​67.​10.​1016/​j.​ipes.​2014.​12.​036
9.
go back to reference Xu D, Li Z, Yang S, Lu Z, Zhang H, Chen W (2018) A classified identification deep-belief network for predicting electric-power load. In: 2018 2nd IEEE conference on energy internet and energy system integration (EI2), pp 1–6 Xu D, Li Z, Yang S, Lu Z, Zhang H, Chen W (2018) A classified identification deep-belief network for predicting electric-power load. In: 2018 2nd IEEE conference on energy internet and energy system integration (EI2), pp 1–6
11.
go back to reference González-Briones A, Hernández G, Corchado J, Omatu S, Mohamad M (2019) Machine learning models for electricity consumption forecasting: a review. IEEE Virtual-Ledgers-Tecnologías DLT/Blockchain y Cripto-IOT. González-Briones A, Hernández G, Corchado J, Omatu S, Mohamad M (2019) Machine learning models for electricity consumption forecasting: a review. IEEE Virtual-Ledgers-Tecnologías DLT/Blockchain y Cripto-IOT.
16.
go back to reference Karunathilake SL, Nagahamulla HR (2017) artificial neural networks for daily electricity demand predicitons of Sri Lanka. In: International conference on advances in ICT for emerging regions (ICTer), pp 128–133 Karunathilake SL, Nagahamulla HR (2017) artificial neural networks for daily electricity demand predicitons of Sri Lanka. In: International conference on advances in ICT for emerging regions (ICTer), pp 128–133
17.
go back to reference Tamizharasi G, Kathiresan S, Sreenivasan K (2014) Energy forecasting using artificial neural networks. Int J Adv Res Electri Electron Instrum Eng 3(3):7568–7576 Tamizharasi G, Kathiresan S, Sreenivasan K (2014) Energy forecasting using artificial neural networks. Int J Adv Res Electri Electron Instrum Eng 3(3):7568–7576
19.
go back to reference Moghaddasi H, Rabiei R, Ahmadzadeh B, Faranbakhsh M (2017) Study on the efficiency of a multi-layer perceptron neural network based on the number of hidden layers and nodes for diagnosing coronary-artery disease. Jentashapir J Health Res. In Press. https://doi.org/10.5812/jjhr.63032 Moghaddasi H, Rabiei R, Ahmadzadeh B, Faranbakhsh M (2017) Study on the efficiency of a multi-layer perceptron neural network based on the number of hidden layers and nodes for diagnosing coronary-artery disease. Jentashapir J Health Res. In Press. https://​doi.​org/​10.​5812/​jjhr.​63032
22.
go back to reference Zhang G, Wang C, Xu B, Grosse R (2019) Three mechanism of weight decay regularization. ArXiv, abs/1810.12281 Zhang G, Wang C, Xu B, Grosse R (2019) Three mechanism of weight decay regularization. ArXiv, abs/1810.12281
23.
go back to reference Botchkarev A (2018) Evaluating performance of regression machine learning models using multiple error metrics in azure machine learning studio. SSRN Electron J 1–16. Evaluating Performance of Regression Machine Learning Models Using Botchkarev A (2018) Evaluating performance of regression machine learning models using multiple error metrics in azure machine learning studio. SSRN Electron J 1–16. Evaluating Performance of Regression Machine Learning Models Using
24.
go back to reference Budiman F (2019) SVM-RBF parameters testing optimization using cross validation and grid search to improve multiclass classification. Sci Visualization 11(1):80–90. https://doi.org/DOI:10.26583/sv.11.1.07 Budiman F (2019) SVM-RBF parameters testing optimization using cross validation and grid search to improve multiclass classification. Sci Visualization 11(1):80–90. https://​doi.​org/​DOI:10.26583/sv.11.1.07
25.
go back to reference Panchal FS, Panchal M (2014) Review on methods of selecting number of hidden nodes in artificial neural network. Int J Comput Sci Mobile Comput 3(11):455–464 Panchal FS, Panchal M (2014) Review on methods of selecting number of hidden nodes in artificial neural network. Int J Comput Sci Mobile Comput 3(11):455–464
27.
go back to reference Fischer A, Igel C (2012) An introduction of restricted boltzmann machines. In: Progress in pattern recognition, image analysis, computer vision, and applications: 17th iberoamerican congress, CIARP 2012. Buenos Aires, Argentina, pp 14–36 Fischer A, Igel C (2012) An introduction of restricted boltzmann machines. In: Progress in pattern recognition, image analysis, computer vision, and applications: 17th iberoamerican congress, CIARP 2012. Buenos Aires, Argentina, pp 14–36
28.
go back to reference Tenaga Nasional Berhad (2006) Tenaga Nasional Berhad Tariff Book Tenaga Nasional Berhad (2006) Tenaga Nasional Berhad Tariff Book
30.
go back to reference Wolpert DH (1996) The lack of a priori distinctions btewen learning algorithm. Neural Comput 1341–1390 Wolpert DH (1996) The lack of a priori distinctions btewen learning algorithm. Neural Comput 1341–1390
31.
go back to reference Stenudd S (2010) Using machine learning in the adaptive control of a smart environment. VTT Publications, Vuorimiehentie, Finland, p 751 Stenudd S (2010) Using machine learning in the adaptive control of a smart environment. VTT Publications, Vuorimiehentie, Finland, p 751
Metadata
Title
Building Energy Management
Authors
Nor Azuana Ramli
Mel Keytingan M. Shapi
Copyright Year
2022
Publisher
Springer Nature Singapore
DOI
https://doi.org/10.1007/978-981-19-0375-5_3