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
A comprehensive risk based security assessment which includes low voltage, line overload and voltage collapse was presented using a relatively new neural network technique called as the generalized regression neural n...A comprehensive risk based security assessment which includes low voltage, line overload and voltage collapse was presented using a relatively new neural network technique called as the generalized regression neural network (GRNN) with incorporation of feature extraction method using principle component analysis. In the risk based security assessment formulation, the failure rate associated to weather condition of each line was used to compute the probability of line outage for a given weather condition and the extent of security violation was represented by a severity function. For low voltage and line overload, continuous severity function was considered due to its ability to zoom in into the effect of near violating contingency. New severity function for voltage collapse using the voltage collapse prediction index was proposed. To reduce the computational burden, a new contingency screening method was proposed using the risk factor so as to select the critical line outages. The risk based security assessment method using GRNN was implemented on a large scale 87-bus power system and the results show that the risk prediction results obtained using GRNN with the incorporation of principal component analysis give better performance in terms of accuracy.展开更多
制冷度日数(Cooling degree days,CDDs)可指示空间制冷能耗与室外热环境,但在全球栅格尺度上同时考虑气温、相对湿度与人口的CDDs分析鲜见报道。据此,本文利用气象、人口、遥感等数据,曼−肯德尔法、相对重要性分析、机器学习等方法在全...制冷度日数(Cooling degree days,CDDs)可指示空间制冷能耗与室外热环境,但在全球栅格尺度上同时考虑气温、相对湿度与人口的CDDs分析鲜见报道。据此,本文利用气象、人口、遥感等数据,曼−肯德尔法、相对重要性分析、机器学习等方法在全球0.25°栅格尺度上开展气温−相对湿度−人口驱动型CDDs时空变化、影响因素与模拟研究。结果表明,①全球基于湿球温度计算的CDDs(CDDs_(wb),CDDs based on wet bulb temperature)在30°N~30°S间除北非与西亚外的不少地区均高于567(℃·d),极高值[1469~2677(℃·d)]主要分布在亚马孙平原、东南亚中南半岛南侧及其以南地区。基于湿球温度与人口计算的CDDs(CDDs based on wet bulb temperature and population,CDDs_(wb_pop))大多低于17×10^(6)(℃·d·人),高值[277×10^(6)~2144×10^(6)(℃·d·人)]主要在恒河平原与印度南端、尼日利亚沿海、越南南北平原与爪哇岛。②1970—2018年CDDs_(wb)与2000—2018年CDDs_(wb_pop)在中高纬度呈现极高年际间变异,全球未来变化趋势多与过去保持强一致性。CDDs_(wb)显著增加(P<0.05)地区主要分布在北非与西亚、澳大利亚、里海东部、印尼西部的一些地区,显著降低区域主要分布在拉美、撒哈拉以南非洲、中国胡焕庸线以南及中南半岛的一些地区。CDDs_(wb_pop)在一些地区显著增加,速率基本小于8×10^(6)(℃·d·人)/a,集中发布在北非、西亚与里海东部的一些地区。③纬度与高程均分别与CDDs_(wb)及其变异系数呈现显著负向与正向偏相关关系(P<0.05);在不同大洲内,年降水量、夏季反照率、增强型植被指数与PM_(2.5)对CDDs_(wb)影响不同,夜间灯光影响不大。CDDs_(wb)实际值与模拟值间R2大多高于0.935,平均绝对误差百分比多小于6.77%,均方根误差在15.63~184.51(℃·d)。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A comprehensive risk based security assessment which includes low voltage, line overload and voltage collapse was presented using a relatively new neural network technique called as the generalized regression neural network (GRNN) with incorporation of feature extraction method using principle component analysis. In the risk based security assessment formulation, the failure rate associated to weather condition of each line was used to compute the probability of line outage for a given weather condition and the extent of security violation was represented by a severity function. For low voltage and line overload, continuous severity function was considered due to its ability to zoom in into the effect of near violating contingency. New severity function for voltage collapse using the voltage collapse prediction index was proposed. To reduce the computational burden, a new contingency screening method was proposed using the risk factor so as to select the critical line outages. The risk based security assessment method using GRNN was implemented on a large scale 87-bus power system and the results show that the risk prediction results obtained using GRNN with the incorporation of principal component analysis give better performance in terms of accuracy.
文摘制冷度日数(Cooling degree days,CDDs)可指示空间制冷能耗与室外热环境,但在全球栅格尺度上同时考虑气温、相对湿度与人口的CDDs分析鲜见报道。据此,本文利用气象、人口、遥感等数据,曼−肯德尔法、相对重要性分析、机器学习等方法在全球0.25°栅格尺度上开展气温−相对湿度−人口驱动型CDDs时空变化、影响因素与模拟研究。结果表明,①全球基于湿球温度计算的CDDs(CDDs_(wb),CDDs based on wet bulb temperature)在30°N~30°S间除北非与西亚外的不少地区均高于567(℃·d),极高值[1469~2677(℃·d)]主要分布在亚马孙平原、东南亚中南半岛南侧及其以南地区。基于湿球温度与人口计算的CDDs(CDDs based on wet bulb temperature and population,CDDs_(wb_pop))大多低于17×10^(6)(℃·d·人),高值[277×10^(6)~2144×10^(6)(℃·d·人)]主要在恒河平原与印度南端、尼日利亚沿海、越南南北平原与爪哇岛。②1970—2018年CDDs_(wb)与2000—2018年CDDs_(wb_pop)在中高纬度呈现极高年际间变异,全球未来变化趋势多与过去保持强一致性。CDDs_(wb)显著增加(P<0.05)地区主要分布在北非与西亚、澳大利亚、里海东部、印尼西部的一些地区,显著降低区域主要分布在拉美、撒哈拉以南非洲、中国胡焕庸线以南及中南半岛的一些地区。CDDs_(wb_pop)在一些地区显著增加,速率基本小于8×10^(6)(℃·d·人)/a,集中发布在北非、西亚与里海东部的一些地区。③纬度与高程均分别与CDDs_(wb)及其变异系数呈现显著负向与正向偏相关关系(P<0.05);在不同大洲内,年降水量、夏季反照率、增强型植被指数与PM_(2.5)对CDDs_(wb)影响不同,夜间灯光影响不大。CDDs_(wb)实际值与模拟值间R2大多高于0.935,平均绝对误差百分比多小于6.77%,均方根误差在15.63~184.51(℃·d)。
