Machine learning for energy consumption prediction and scheduling in smart buildings

被引:64
作者
Bourhnane, Safae [1 ]
Abid, Mohamed Riduan [2 ]
Lghoul, Rachid [2 ]
Zine-Dine, Khalid [3 ]
Elkamoun, Najib [1 ]
Benhaddou, Driss [4 ]
机构
[1] Chouaib Doukkali Univ, Fac Sci, LAROSERI Lab, El Jadida, Morocco
[2] Al Akhawayn Univ, Sch Sci & Engn, Ifrane, Morocco
[3] Mohammed V Univ, FSR, Rabat, Morocco
[4] Univ Houston, Sch Engn & Technol, Houston, TX USA
来源
SN APPLIED SCIENCES | 2020年 / 2卷 / 02期
关键词
Smart Grids; Smart buildings; Renewable energy; ANN; GA; CompactRIO; NEURAL-NETWORKS;
D O I
10.1007/s42452-020-2024-9
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Predicting energy consumption in Smart Buildings (SB), and scheduling it, is crucial for deploying Energy-efficient Management Systems. Most important, this constitutes a key aspect in the promising Smart Grids technology, whereby loads need to be predicted and scheduled in real-time to cope for the strongly coupled variance between energy demand and cost. Several approaches and models have been adopted for energy consumption prediction and scheduling. In this paper, we investigated available models and opted for machine learning. Namely, we use Artificial Neural Networks (ANN) along with Genetic Algorithms. We deployed our models in a real-world SB testbed. We used CompactRIO for ANN implementation. The proposed models are trained and validated using real-world data collected from a PV installation along with SB electrical appliances. Though our model exhibited a modest prediction accuracy, which is due to the small size of the data set, we strongly recommend our model as a blue-print for researchers willing to deploy real-world SB testbeds and investigate machine learning as a promising venue for energy consumption prediction and scheduling.
引用
收藏
页数:10
相关论文
共 22 条
  • [1] Abid MR, 2017, AFRICON, P856, DOI 10.1109/AFRCON.2017.8095594
  • [2] A review of data-driven building energy consumption prediction studies
    Amasyali, Kadir
    El-Gohary, Nora M.
    [J]. RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2018, 81 : 1192 - 1205
  • [3] [Anonymous], GENETIC ALGORITHMS
  • [4] [Anonymous], 2016, HOM EN CONS
  • [5] [Anonymous], LIVESCIENCE
  • [6] [Anonymous], MOVING AVERAGE MA
  • [7] [Anonymous], 6 INT REN SUST EN C
  • [8] [Anonymous], 2017, WHAT IS MACH LEARN D
  • [9] Fuzzy Logic-Based Energy Management System Design for Residential Grid-Connected Microgrids
    Arcos-Aviles, Diego
    Pascual, Julio
    Marroyo, Luis
    Sanchis, Pablo
    Guinjoan, Francesc
    [J]. IEEE TRANSACTIONS ON SMART GRID, 2018, 9 (02) : 530 - 543
  • [10] Atin, 2013, Energy managment system