机译:一种新颖的多时间尺度电力需求预测模型:从短期到中期
Florida Int Univ, Sch Comp & Informat Sci, Miami, FL 33199 USA;
Carnegie Mellon Univ, Joint Inst Engn, Sun Yat Sen Univ, Guangzhou 510006, Guangdong, Peoples R China|Carnegie Mellon Univ, Dept Elect & Comp Engn, Pittsburgh, PA 15213 USA|SYSU CMU Shunde Int Joint Res Inst, Shunde, Guangdong, Peoples R China|SYSU, Sch Elect & Informat Technol, Guangzhou, Guangdong, Peoples R China;
Univ British Columbia, Dept Elect & Comp Engn, Vancouver, BC, Canada;
Florida Int Univ, Sch Comp & Informat Sci, Miami, FL 33199 USA;
Florida Int Univ, Dept Elect & Comp Engn, Miami, FL 33174 USA;
Carnegie Mellon Univ, Joint Inst Engn, Sun Yat Sen Univ, Guangzhou 510006, Guangdong, Peoples R China|Carnegie Mellon Univ, Dept Elect & Comp Engn, Pittsburgh, PA 15213 USA|SYSU CMU Shunde Int Joint Res Inst, Shunde, Guangdong, Peoples R China|SYSU, Sch Elect & Informat Technol, Guangzhou, Guangdong, Peoples R China;
Autoregressive model; Moving-average model; Time-series forecasting; Electric power demand forecast; Akaike information criterion; Bayesian information criterion;
机译:中期电力需求的概率预测:时间序列模型的比较
机译:使用汇总的需求数据在澳大利亚昆士兰州使用MARS,SVR和ARIMA模型进行短期电力需求预测
机译:基于二维小波的Sdp模型对维多利亚州每日峰值电力需求进行建模和短期预测
机译:基于经济电力传输模型的中期电力需求预测
机译:使用人工神经网络通过智能电气负载控制峰值需求,从而进行短期峰值需求预测。
机译:混合智能建模的酒店短期能源需求预测
机译:在多时视野上的短期风力预测模型的强大评估