A combined model based on principal components analysis (PCA) and generalized regression neural network (GRNN) was adopted to forecast electricity price in day-ahead electricity market. PCA was applied to mine the mai...A combined model based on principal components analysis (PCA) and generalized regression neural network (GRNN) was adopted to forecast electricity price in day-ahead electricity market. PCA was applied to mine the main influence on day-ahead price, avoiding the strong correlation between the input factors that might influence electricity price, such as the load of the forecasting hour, other history loads and prices, weather and temperature; then GRNN was employed to forecast electricity price according to the main information extracted by PCA. To prove the efficiency of the combined model, a case from PJM (Pennsylvania-New Jersey-Maryland) day-ahead electricity market was evaluated. Compared to back-propagation (BP) neural network and standard GRNN, the combined method reduces the mean absolute percentage error about 3%.展开更多
基金Project(70671039) supported by the National Natural Science Foundation of China
文摘A combined model based on principal components analysis (PCA) and generalized regression neural network (GRNN) was adopted to forecast electricity price in day-ahead electricity market. PCA was applied to mine the main influence on day-ahead price, avoiding the strong correlation between the input factors that might influence electricity price, such as the load of the forecasting hour, other history loads and prices, weather and temperature; then GRNN was employed to forecast electricity price according to the main information extracted by PCA. To prove the efficiency of the combined model, a case from PJM (Pennsylvania-New Jersey-Maryland) day-ahead electricity market was evaluated. Compared to back-propagation (BP) neural network and standard GRNN, the combined method reduces the mean absolute percentage error about 3%.
文摘体内血糖测试虽然可以准确测定黄酒的血糖生成指数(glycemic index,GI),但相关食品营养学研究在食品工业中的应用却因效率低、成本高、可重复性差以及涉及伦理道德问题而受限严重。该研究构建了黄酒的半动态体外消化模型,使用不同甜型的黄酒进行体外消化实验,绘制相应的消化曲线,对实验结果进行数学拟合,推导出一系列黄酒半动态体外消化参数,并测得不同甜型黄酒的人体体内真实GI。通过主成分分析、偏最小二乘回归(partial least squares regression,PLSR)和Lasso回归等多元统计方法,建立基于半动态体外消化参数的黄酒GI预测模型。结果表明,PLSR预测模型良好,误差率<10%,预测方程为GI=0.323X+64.898(R^(2)=0.964),其中X表示黄酒体外消化胃阶段第一次胃排空时葡萄糖变化曲线下面积和单次胃排空时间的比值。该研究所提出的半动态体外消化模型和GI预测方程为黄酒的饮后血糖响应提供了一种快速、准确的分析方法。
