IDEAS home Printed from https://ideas.repec.org/a/igg/jsir00/v14y2023i1p1-13.html
   My bibliography  Save this article

An Optimal Neural Network for Hourly and Daily Energy Consumption Prediction in Buildings

Author

Listed:
  • Fazli Wahid

    (Universiti Tun Hussein Onn, Malaysia)

  • Rozaida Ghazali

    (Universiti Tun Hussein Onn, Malaysia)

  • Lokman Hakim Ismail

    (Universiti Tun Hussein Onn, Malaysia)

  • Ali M. Algarwi Aseere

    (College of Computer Science, King Khalid University, Abha, Saudi Arabia)

Abstract

In this work, hourly and daily energy consumption prediction has been carried out using multi-layer feed forward neural network. The network designed in the proposed architecture has three layers, namely input layer, hidden layer, and output layer. The input layer had eight neurons, output layer had one neuron, and the number of neurons in the hidden layer was varied to find an optimal number for accurate prediction. Different parameters of the neural network were varied repeatedly, and the prediction accuracy was observed for each combination of different parameters to find an optimized combination of different parameters. For hourly energy consumption prediction, a total of six weeks data (September 1 to October 12, 2004) of 10 residential buildings has been used whereas for daily energy consumption prediction, a total of 52 weeks data (January 2004 to December 2004) of 30 residential buildings has been used. To evaluate the performance of the proposed approach, different performance evaluation measurements were applied.

Suggested Citation

  • Fazli Wahid & Rozaida Ghazali & Lokman Hakim Ismail & Ali M. Algarwi Aseere, 2023. "An Optimal Neural Network for Hourly and Daily Energy Consumption Prediction in Buildings," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 14(1), pages 1-13, January.
    • Handle: RePEc:igg:jsir00:v:14:y:2023:i:1:p:1-13
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJSIR.316649
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ying Lin & Zhi-Hui Zhan & Jun Zhang, 2014. "Optimizing Power Electronic Circuit Design with Uniform Search Range: An Orthogonal Learning Particle Swarm Optimization Approach with Predictive Solution Strategy," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 5(3), pages 41-59, July.
    2. Qiang Wang & Hai-Lin Liu, 2017. "Optimized Base Station Sleeping and Renewable Energy Procurement Scheme Using PSO," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 8(1), pages 54-73, January.
    3. Patwal, Rituraj Singh & Narang, Nitin & Garg, Harish, 2018. "A novel TVAC-PSO based mutation strategies algorithm for generation scheduling of pumped storage hydrothermal system incorporating solar units," Energy, Elsevier, vol. 142(C), pages 822-837.
    4. Garg, Harish, 2016. "A hybrid PSO-GA algorithm for constrained optimization problems," Applied Mathematics and Computation, Elsevier, vol. 274(C), pages 292-305.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ahmed A. Ewees & Mohammed A. A. Al-qaness & Laith Abualigah & Diego Oliva & Zakariya Yahya Algamal & Ahmed M. Anter & Rehab Ali Ibrahim & Rania M. Ghoniem & Mohamed Abd Elaziz, 2021. "Boosting Arithmetic Optimization Algorithm with Genetic Algorithm Operators for Feature Selection: Case Study on Cox Proportional Hazards Model," Mathematics, MDPI, vol. 9(18), pages 1-22, September.
    2. Chen, Shuixia & Wang, Jian-qiang & Zhang, Hong-yu, 2019. "A hybrid PSO-SVM model based on clustering algorithm for short-term atmospheric pollutant concentration forecasting," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 41-54.
    3. Chun-Yao Lee & Guang-Lin Zhuo, 2021. "A Hybrid Whale Optimization Algorithm for Global Optimization," Mathematics, MDPI, vol. 9(13), pages 1-19, June.
    4. Zaher Mundher Yaseen & Mohammad Ehteram & Md. Shabbir Hossain & Chow Ming Fai & Suhana Binti Koting & Nuruol Syuhadaa Mohd & Wan Zurina Binti Jaafar & Haitham Abdulmohsin Afan & Lai Sai Hin & Nuratiah, 2019. "A Novel Hybrid Evolutionary Data-Intelligence Algorithm for Irrigation and Power Production Management: Application to Multi-Purpose Reservoir Systems," Sustainability, MDPI, vol. 11(7), pages 1-28, April.
    5. Sakthivel, V.P. & Thirumal, K. & Sathya, P.D., 2022. "Short term scheduling of hydrothermal power systems with photovoltaic and pumped storage plants using quasi-oppositional turbulent water flow optimization," Renewable Energy, Elsevier, vol. 191(C), pages 459-492.
    6. Adane Abebaw Gessesse & Rajashree Mishra & Mitali Madhumita Acharya & Kedar Nath Das, 2020. "Genetic algorithm based fuzzy programming approach for multi-objective linear fractional stochastic transportation problem involving four-parameter Burr distribution," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(1), pages 93-109, February.
    7. Basu, Mousumi, 2022. "Fuel constrained short-term hydrothermal generation scheduling," Energy, Elsevier, vol. 239(PD).
    8. Zhang, Rongda & Wei, Jing & Zhao, Xiaoli & Liu, Yang, 2022. "Economic and environmental benefits of the integration between carbon sequestration and underground gas storage," Energy, Elsevier, vol. 260(C).
    9. Yassin Belkourchia & Mohamed Zeriab Es-Sadek & Lahcen Azrar, 2023. "New Hybrid Perturbed Projected Gradient and Simulated Annealing Algorithms for Global Optimization," Journal of Optimization Theory and Applications, Springer, vol. 197(2), pages 438-475, May.
    10. Touqeer Ahmed Jumani & Mohd Wazir Mustafa & Nawaf N. Hamadneh & Samer H. Atawneh & Madihah Md. Rasid & Nayyar Hussain Mirjat & Muhammad Akram Bhayo & Ilyas Khan, 2020. "Computational Intelligence-Based Optimization Methods for Power Quality and Dynamic Response Enhancement of ac Microgrids," Energies, MDPI, vol. 13(16), pages 1-22, August.
    11. Pavan Kumar Singh & Nitin Singh & Richa Negi, 2021. "Short-Term Wind Power Prediction Using Hybrid Auto Regressive Integrated Moving Average Model and Dynamic Particle Swarm Optimization," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 15(2), pages 111-138, April.
    12. Kim, H.J. & Kim, M.K., 2023. "A novel deep learning-based forecasting model optimized by heuristic algorithm for energy management of microgrid," Applied Energy, Elsevier, vol. 332(C).
    13. Li, Chao & Zhai, Rongrong & Yang, Yongping & Patchigolla, Kumar & Oakey, John E. & Turner, Peter, 2019. "Annual performance analysis and optimization of a solar tower aided coal-fired power plant," Applied Energy, Elsevier, vol. 237(C), pages 440-456.
    14. Brayan A. Atoccsa & David W. Puma & Daygord Mendoza & Estefany Urday & Cristhian Ronceros & Modesto T. Palma, 2024. "Optimization of Ampacity in High-Voltage Underground Cables with Thermal Backfill Using Dynamic PSO and Adaptive Strategies," Energies, MDPI, vol. 17(5), pages 1-19, February.
    15. Luo, Qifang & Yang, Xiao & Zhou, Yongquan, 2019. "Nature-inspired approach: An enhanced moth swarm algorithm for global optimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 159(C), pages 57-92.
    16. Hu, Yusha & Li, Jigeng & Hong, Mengna & Ren, Jingzheng & Lin, Ruojue & Liu, Yue & Liu, Mengru & Man, Yi, 2019. "Short term electric load forecasting model and its verification for process industrial enterprises based on hybrid GA-PSO-BPNN algorithm—A case study of papermaking process," Energy, Elsevier, vol. 170(C), pages 1215-1227.
    17. Xiang, Shihu & Yang, Jun, 2023. "A novel adaptive deployment method for the single-target tracking of mobile wireless sensor networks," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    18. Yan, Zheping & Zhang, Jinzhong & Zeng, Jia & Tang, Jialing, 2021. "Nature-inspired approach: An enhanced whale optimization algorithm for global optimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 185(C), pages 17-46.
    19. Máximo Méndez & Mariano Frutos & Fabio Miguel & Ricardo Aguasca-Colomo, 2020. "TOPSIS Decision on Approximate Pareto Fronts by Using Evolutionary Algorithms: Application to an Engineering Design Problem," Mathematics, MDPI, vol. 8(11), pages 1-27, November.
    20. Aqsa Naeem & Naveed Ul Hassan & Chau Yuen & S. M. Muyeen, 2019. "Maximizing the Economic Benefits of a Grid-Tied Microgrid Using Solar-Wind Complementarity," Energies, MDPI, vol. 12(3), pages 1-22, January.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jsir00:v:14:y:2023:i:1:p:1-13. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.