%0 Journal Article
%T Load-forecasting model based on normalized Gaussian pLSA collaborative filtering<br>基于标准化高斯pLSA协同过滤的用电量预测模型
%A LIU Yue-qian
%A YAO Hong-yu
%A <br>刘粤钳
%A 姚红玉
%J 控制理论与应用
%D 2008
%I 
%X To some extent the existing long-term load-forecasting algorithms have their limitations because the variables influencing the output of the complex non-linear system are too many to be described. By combining the probabilistic Latent Semantic Analysis (pLSA) that can cluster random data into respective aspects and content-based collaborative filtering, a novel load forecasting model based on normalized Gaussian probabilistic latent semantic analysis collaborative filtering is proposed in order to avoid seeking and describing of the hidden variables mentioned above. Simulating experiments via MATLAB show that this method gains the advantage in accuracy over neural network and grey prediction.
%K probabilistic latent semantic analysis
%K collaborative filtering
%K aspect model
%K load forecasting model<br>概率潜在语义分析
%K 协同过滤
%K 示象模型
%K 用电量预测模型
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=970898A57DFC021F93AB51667BAED7F7&aid=D32FEFC871EE43079A2693148B4A36A0&yid=67289AFF6305E306&vid=C5154311167311FE&iid=94C357A881DFC066&sid=0FBB0D015A3E9A88&eid=796A97DD793AE4A8&journal_id=1000-8152&journal_name=控制理论与应用&referenced_num=0&reference_num=9