IDEAS home Printed from https://ideas.repec.org/a/eee/enepol/v23y1995i12p1097-1104.html
   My bibliography  Save this article

Forecasting electric energy consumption using neural networks

Author

Listed:
  • Javeed Nizami, SSAK
  • Al-Garni, Ahmed Z

Abstract

No abstract is available for this item.

Suggested Citation

  • Javeed Nizami, SSAK & Al-Garni, Ahmed Z, 1995. "Forecasting electric energy consumption using neural networks," Energy Policy, Elsevier, vol. 23(12), pages 1097-1104, December.
    • Handle: RePEc:eee:enepol:v:23:y:1995:i:12:p:1097-1104
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/0301-4215(95)00116-6
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Al-Garni, Ahmed Z. & Zubair, Syed M. & Nizami, Javeed S., 1994. "A regression model for electric-energy-consumption forecasting in Eastern Saudi Arabia," Energy, Elsevier, vol. 19(10), pages 1043-1049.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jinrong Wu & Su Nguyen & Damminda Alahakoon & Daswin De Silva & Nishan Mills & Prabod Rathnayaka & Harsha Moraliyage & Andrew Jennings, 2024. "A Comparative Analysis of Machine Learning-Based Energy Baseline Models across Multiple Building Types," Energies, MDPI, vol. 17(6), pages 1-18, March.
    2. Jebaraj, S. & Iniyan, S., 2006. "A review of energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 10(4), pages 281-311, August.
    3. Konstantinos Papageorgiou & Elpiniki I. Papageorgiou & Katarzyna Poczeta & Dionysis Bochtis & George Stamoulis, 2020. "Forecasting of Day-Ahead Natural Gas Consumption Demand in Greece Using Adaptive Neuro-Fuzzy Inference System," Energies, MDPI, vol. 13(9), pages 1-32, May.
    4. Marek Dudzik, 2020. "Towards Characterization of Indoor Environment in Smart Buildings: Modelling PMV Index Using Neural Network with One Hidden Layer," Sustainability, MDPI, vol. 12(17), pages 1-37, August.
    5. Nibedita Mahanta & Ruma Talukdar, 2024. "Forecasting of Electricity Consumption by Seasonal Autoregressive Integrated Moving Average Model in Assam, India," International Journal of Energy Economics and Policy, Econjournals, vol. 14(5), pages 393-400, September.
    6. Ruma Talukdar & Nibedita Mahanta, 2023. "Forecasting of Domestic Electricity Consumption in Assam, India," International Journal of Energy Economics and Policy, Econjournals, vol. 13(5), pages 229-235, September.
    7. Zhao, Hai-xiang & Magoulès, Frédéric, 2012. "A review on the prediction of building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3586-3592.
    8. Azadeh, A. & Saberi, M. & Seraj, O., 2010. "An integrated fuzzy regression algorithm for energy consumption estimation with non-stationary data: A case study of Iran," Energy, Elsevier, vol. 35(6), pages 2351-2366.
    9. Deb, Chirag & Zhang, Fan & Yang, Junjing & Lee, Siew Eang & Shah, Kwok Wei, 2017. "A review on time series forecasting techniques for building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 902-924.
    10. John Cooper, 1999. "Artificial neural networks versus multivariate statistics: An application from economics," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(8), pages 909-921.
    11. Anam-Nawaz Khan & Naeem Iqbal & Atif Rizwan & Rashid Ahmad & Do-Hyeun Kim, 2021. "An Ensemble Energy Consumption Forecasting Model Based on Spatial-Temporal Clustering Analysis in Residential Buildings," Energies, MDPI, vol. 14(11), pages 1-25, May.
    12. Zihao Li & Daniel Friedrich & Gareth P. Harrison, 2020. "Demand Forecasting for a Mixed-Use Building Using Agent-Schedule Information with a Data-Driven Model," Energies, MDPI, vol. 13(4), pages 1-20, February.
    13. Macedo, M.N.Q. & Galo, J.J.M. & de Almeida, L.A.L. & de C. Lima, A.C., 2015. "Demand side management using artificial neural networks in a smart grid environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 128-133.
    14. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    15. Ji, Li-Qun, 2015. "An assessment of agricultural residue resources for liquid biofuel production in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 561-575.
    16. Buratti, C. & Barbanera, M. & Palladino, D., 2014. "An original tool for checking energy performance and certification of buildings by means of Artificial Neural Networks," Applied Energy, Elsevier, vol. 120(C), pages 125-132.
    17. Federico Divina & Miguel García Torres & Francisco A. Goméz Vela & José Luis Vázquez Noguera, 2019. "A Comparative Study of Time Series Forecasting Methods for Short Term Electric Energy Consumption Prediction in Smart Buildings," Energies, MDPI, vol. 12(10), pages 1-23, May.
    18. Federico Divina & Aude Gilson & Francisco Goméz-Vela & Miguel García Torres & José F. Torres, 2018. "Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting," Energies, MDPI, vol. 11(4), pages 1-31, April.

    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. Sánchez, Gustavo Crespo & Monteagudo Yanes, José Pedro & Pérez, Milagros Montesino & Cabrera Sánchez, Jorge Luis & Padrón, Arturo Padrón & Haeseldonckx, Dries, 2020. "Efficiency in electromechanical drive motors and energy performance indicators for implementing a management system in balanced animal feed manufacturing," Energy, Elsevier, vol. 194(C).
    2. Yuan, Chaoqing & Liu, Sifeng & Fang, Zhigeng, 2016. "Comparison of China's primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM(1,1) model," Energy, Elsevier, vol. 100(C), pages 384-390.
    3. Jimyung Kang & Soonwoo Lee, 2018. "Data-Driven Prediction of Load Curtailment in Incentive-Based Demand Response System," Energies, MDPI, vol. 11(11), pages 1-14, October.
    4. Liu, Yuan & He, Li & Shen, Jing, 2017. "Optimization-based provincial hybrid renewable and non-renewable energy planning – A case study of Shanxi, China," Energy, Elsevier, vol. 128(C), pages 839-856.
    5. Khan, Ahsan Raza & Mahmood, Anzar & Safdar, Awais & Khan, Zafar A. & Khan, Naveed Ahmed, 2016. "Load forecasting, dynamic pricing and DSM in smart grid: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 1311-1322.
    6. Hu, Ting & Huang, Xin, 2019. "A novel locally adaptive method for modeling the spatiotemporal dynamics of global electric power consumption based on DMSP-OLS nighttime stable light data," Applied Energy, Elsevier, vol. 240(C), pages 778-792.
    7. Salma Hamad Almuhaini & Nahid Sultana, 2023. "Bayesian-Optimization-Based Long Short-Term Memory (LSTM) Super Learner Approach for Modeling Long-Term Electricity Consumption," Sustainability, MDPI, vol. 15(18), pages 1-23, September.
    8. Jeong, Jaewook & Hong, Taehoon & Ji, Changyoon & Kim, Jimin & Lee, Minhyun & Jeong, Kwangbok, 2016. "Development of an integrated energy benchmark for a multi-family housing complex using district heating," Applied Energy, Elsevier, vol. 179(C), pages 1048-1061.
    9. Wen-Ze Wu & Chong Liu & Wanli Xie & Mark Goh & Tao Zhang, 2023. "Predictive analysis of the industrial water-waste-energy system using an optimised grey approach: A case study in China," Energy & Environment, , vol. 34(5), pages 1639-1656, August.
    10. Y, Kiguchi & Y, Heo & M, Weeks & R, Choudhary, 2019. "Predicting intra-day load profiles under time-of-use tariffs using smart meter data," Energy, Elsevier, vol. 173(C), pages 959-970.
    11. Radhi, Hassan & Sharples, Stephen, 2013. "Quantifying the domestic electricity consumption for air-conditioning due to urban heat islands in hot arid regions," Applied Energy, Elsevier, vol. 112(C), pages 371-380.
    12. Kaneko, Nanae & Fujimoto, Yu & Hayashi, Yasuhiro, 2022. "Sensitivity analysis of factors relevant to extreme imbalance between procurement plans and actual demand: Case study of the Japanese electricity market," Applied Energy, Elsevier, vol. 313(C).
    13. Salma Hamad Almuhaini & Nahid Sultana, 2023. "Forecasting Long-Term Electricity Consumption in Saudi Arabia Based on Statistical and Machine Learning Algorithms to Enhance Electric Power Supply Management," Energies, MDPI, vol. 16(4), pages 1-28, February.
    14. Jebaraj, S. & Iniyan, S., 2006. "A review of energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 10(4), pages 281-311, August.
    15. Nsangou, Jean Calvin & Kenfack, Joseph & Nzotcha, Urbain & Ngohe Ekam, Paul Salomon & Voufo, Joseph & Tamo, Thomas T., 2022. "Explaining household electricity consumption using quantile regression, decision tree and artificial neural network," Energy, Elsevier, vol. 250(C).
    16. Tso, Geoffrey K.F. & Yau, Kelvin K.W., 2007. "Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks," Energy, Elsevier, vol. 32(9), pages 1761-1768.
    17. Hu, Ting & Wang, Ting & Yan, Qingyun & Chen, Tiexi & Jin, Shuanggen & Hu, Jun, 2022. "Modeling the spatiotemporal dynamics of global electric power consumption (1992–2019) by utilizing consistent nighttime light data from DMSP-OLS and NPP-VIIRS," Applied Energy, Elsevier, vol. 322(C).
    18. Hong, Taehoon & Koo, Choongwan & Jeong, Kwangbok, 2012. "A decision support model for reducing electric energy consumption in elementary school facilities," Applied Energy, Elsevier, vol. 95(C), pages 253-266.
    19. Kaneko, Nanae & Fujimoto, Yu & Kabe, Satoshi & Hayashida, Motonari & Hayashi, Yasuhiro, 2020. "Sparse modeling approach for identifying the dominant factors affecting situation-dependent hourly electricity demand," Applied Energy, Elsevier, vol. 265(C).
    20. Zhang, Ming & Mu, Hailin & Li, Gang & Ning, Yadong, 2009. "Forecasting the transport energy demand based on PLSR method in China," Energy, Elsevier, vol. 34(9), pages 1396-1400.

    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:eee:enepol:v:23:y:1995:i:12:p:1097-1104. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/enpol .

    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.