Forecasting Building Electric Consumption Patterns Through Statistical Methods
Author:
Publisher
Springer International Publishing
Link
http://link.springer.com/content/pdf/10.1007/978-3-030-32033-1_16
Reference20 articles.
1. Abedinia, O., Amjady, N., Zareipour, H.: A new feature selection technique for load and price forecast of electrical power systems. IEEE Trans. Power Syst. 32(1), 62–74 (2017). https://doi.org/10.1109/TPWRS.2016.2556620
2. Amber, K.P., Ahmad, R., Aslam, M.W., Kousar, A., Usman, M., Khan, M.S.: Intelligent techniques for forecasting electricity consumption of buildings. Energy 157, 886–893 (2018). https://doi.org/10.1016/j.energy.2018.05.155
3. Bianchi, F.M., Santis, E.D.E., Rizzi, A., Sadeghian, A.: Short-term electric load forecasting using echo state networks and PCA decomposition. IEEE Access 3, 1931–1943 (2015). https://doi.org/10.1109/ACCESS.2015.2485943
4. Contreras, J., Espinola, R., Nogales, F.J., Conejo, A.J.: ARIMA models to predict next-day electricity prices. IEEE Trans. Power Syst. 22(9), 57–57 (2002). https://doi.org/10.1016/j.energy.2018.05.155 . http://ieeexplore.ieee.org/document/4312577/
5. Ding, N., Benoit, C., Foggia, G., Bésanger, Y., Wurtz, F.: Neural network-based model design for short-term load forecast in distribution systems. IEEE Trans. Power Syst. 31(1), 72–81 (2016). https://doi.org/10.1109/TPWRS.2015.2390132
Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Three-Phase Load Prediction-Based Hybrid Convolution Neural Network Combined Bidirectional Long Short-Term Memory in Solar Power Plant;International Transactions on Electrical Energy Systems;2022-09-16
2. Analysis of Electricity Consumption in Poland Using Prediction Models and Neural Networks;Energies;2021-10-14
3. A new interval prediction methodology for short-term electric load forecasting based on pattern recognition;Applied Energy;2021-09
4. A comparison of sizing methods for a long-term renewable hybrid system. Case study: Galapagos Islands 2031;Sustainable Energy & Fuels;2021
5. A Time-Series Treatment Method to Obtain Electrical Consumption Patterns for Anomalies Detection Improvement in Electrical Consumption Profiles;Energies;2020-02-26
1.学者识别学者识别
2.学术分析学术分析
3.人才评估人才评估
"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370
www.globalauthorid.com
TOP
Copyright © 2019-2024 北京同舟云网络信息技术有限公司 京公网安备11010802033243号 京ICP备18003416号-3