Medium-term forecasting is an important category of electric load forecasting that covers a time span of up to one year ahead. It suits outage and maintenance planning, as well as load switching operation. We propose ...Medium-term forecasting is an important category of electric load forecasting that covers a time span of up to one year ahead. It suits outage and maintenance planning, as well as load switching operation. We propose a new methodol-ogy that uses hourly daily loads to predict the next year hourly loads, and hence predict the peak loads expected to be reached in the next coming year. The technique is based on implementing multivariable regression on previous year's hourly loads. Three regression models are investigated in this research: the linear, the polynomial, and the exponential power. The proposed models are applied to real loads of the Jordanian power system. Results obtained using the pro-posed methods showed that their performance is close and they outperform results obtained using the widely used ex-ponential regression technique. Moreover, peak load prediction has about 90% accuracy using the proposed method-ology. The methods are generic and simple and can be implemented to hourly loads of any power system. No extra in-formation other than the hourly loads is required.展开更多
This paper presents a technique for Medium Term Load Forecasting (MTLF) using Particle Swarm Optimization (PSO) algorithm based on Least Squares Regression Methods to forecast the electric loads of the Jordanian grid ...This paper presents a technique for Medium Term Load Forecasting (MTLF) using Particle Swarm Optimization (PSO) algorithm based on Least Squares Regression Methods to forecast the electric loads of the Jordanian grid for year of 2015. Linear, quadratic and exponential forecast models have been examined to perform this study and compared with the Auto Regressive (AR) model. MTLF models were influenced by the weather which should be considered when predicting the future peak load demand in terms of months and weeks. The main contribution for this paper is the conduction of MTLF study for Jordan on weekly and monthly basis using real data obtained from National Electric Power Company NEPCO. This study is aimed to develop practical models and algorithm techniques for MTLF to be used by the operators of Jordan power grid. The results are compared with the actual peak load data to attain minimum percentage error. The value of the forecasted weekly and monthly peak loads obtained from these models is examined using Least Square Error (LSE). Actual reported data from NEPCO are used to analyze the performance of the proposed approach and the results are reported and compared with the results obtained from PSO algorithm and AR model.展开更多
针对中长期电力负荷序列噪声含量高、难以直接提取序列周期规律从而影响预测精度的问题,提出了一种基于完全自适应噪声集合经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)和奇异谱分析(sin...针对中长期电力负荷序列噪声含量高、难以直接提取序列周期规律从而影响预测精度的问题,提出了一种基于完全自适应噪声集合经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)和奇异谱分析(singular spectrum analysis,SSA)双重分解的双向长短时记忆网络(bidirectional long and short time memory,BiLSTM)预测模型。首先,采用CEEMDAN对历史负荷进行分解,以得到若干个周期规律更为清晰的子序列;再利用多尺度熵(multiscale entropy,MSE)计算所有子序列的复杂程度,根据不同时间尺度上的样本熵值将相似的子序列重构聚合;然后,利用SSA去噪的功能,对高度复杂的新序列进行二次分解,去除序列中的噪声并提取更为主要的规律,从而进一步提高中长序列预测精度;再将得到的最终一组子序列输入BiLSTM进行预测;最后,考虑到天气、节假日等外部因素对电力负荷的影响,提出了一种误差修正技术。选取了巴拿马某地区的用电负荷进行实验,实验结果表明,经过双重分解可以将均方根误差降低87.4%;预测未来一年的负荷序列时,采用的BiLSTM模型将拟合系数最高提高2.5%;所提出的误差修正技术可将均方根误差降低9.7%。展开更多
跨介质航行体入水瞬间会受到巨大的冲击载荷,极易导致结构破坏甚至内部器件失灵。为发展有效的降载方法,本文基于VOF(Volume of Fluid)多相流模型,研究头部喷气航行体入水过程的载荷特性和流体动力特性,分析喷气压力、喷气高度对降载效...跨介质航行体入水瞬间会受到巨大的冲击载荷,极易导致结构破坏甚至内部器件失灵。为发展有效的降载方法,本文基于VOF(Volume of Fluid)多相流模型,研究头部喷气航行体入水过程的载荷特性和流体动力特性,分析喷气压力、喷气高度对降载效果的影响,并探索头部喷气降载方法有效性的入水速度范围。研究结果表明,头部喷气使自由液面下凹形成空腔,并能极大地降低航行体所受阻力和冲击力,延缓了航行体撞水时间,从而实现冲击载荷控制;喷气压力和喷气高度对入水空泡形态及冲击压力峰值的影响都不大,合理的选择既能达到降载效果又能节约喷气量;入水速度为50 m/s时,降载量高达76.51%,但当入水速度为300 m/s时,降载量仅为39.92%。因此,针对高亚声速跨介质入水问题,需进一步探索主被动相结合的复合降载方法。展开更多
项目供热面积约1.14×10^(5)m^(2),总热负荷7000 k W,采用中深层地热供热,平均热负荷为4859.09 kW,项目年耗热量为62959.68 GJ。热源为1口水热型地热井,按照“一采一灌、同层回灌、取热不耗水”的模式进行建设,配套回灌井1口。利用...项目供热面积约1.14×10^(5)m^(2),总热负荷7000 k W,采用中深层地热供热,平均热负荷为4859.09 kW,项目年耗热量为62959.68 GJ。热源为1口水热型地热井,按照“一采一灌、同层回灌、取热不耗水”的模式进行建设,配套回灌井1口。利用中深层地热水作为供热热源,冬季提供45℃/40℃采暖循环水,采用“板换直供+热泵机组调峰”的方式为项目供热。按照30年运行期测算,中深层地热方案相比于天然气锅炉方案可节省费用1.127738×10^(8)元。展开更多
文摘Medium-term forecasting is an important category of electric load forecasting that covers a time span of up to one year ahead. It suits outage and maintenance planning, as well as load switching operation. We propose a new methodol-ogy that uses hourly daily loads to predict the next year hourly loads, and hence predict the peak loads expected to be reached in the next coming year. The technique is based on implementing multivariable regression on previous year's hourly loads. Three regression models are investigated in this research: the linear, the polynomial, and the exponential power. The proposed models are applied to real loads of the Jordanian power system. Results obtained using the pro-posed methods showed that their performance is close and they outperform results obtained using the widely used ex-ponential regression technique. Moreover, peak load prediction has about 90% accuracy using the proposed method-ology. The methods are generic and simple and can be implemented to hourly loads of any power system. No extra in-formation other than the hourly loads is required.
文摘This paper presents a technique for Medium Term Load Forecasting (MTLF) using Particle Swarm Optimization (PSO) algorithm based on Least Squares Regression Methods to forecast the electric loads of the Jordanian grid for year of 2015. Linear, quadratic and exponential forecast models have been examined to perform this study and compared with the Auto Regressive (AR) model. MTLF models were influenced by the weather which should be considered when predicting the future peak load demand in terms of months and weeks. The main contribution for this paper is the conduction of MTLF study for Jordan on weekly and monthly basis using real data obtained from National Electric Power Company NEPCO. This study is aimed to develop practical models and algorithm techniques for MTLF to be used by the operators of Jordan power grid. The results are compared with the actual peak load data to attain minimum percentage error. The value of the forecasted weekly and monthly peak loads obtained from these models is examined using Least Square Error (LSE). Actual reported data from NEPCO are used to analyze the performance of the proposed approach and the results are reported and compared with the results obtained from PSO algorithm and AR model.
文摘针对中长期电力负荷序列噪声含量高、难以直接提取序列周期规律从而影响预测精度的问题,提出了一种基于完全自适应噪声集合经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)和奇异谱分析(singular spectrum analysis,SSA)双重分解的双向长短时记忆网络(bidirectional long and short time memory,BiLSTM)预测模型。首先,采用CEEMDAN对历史负荷进行分解,以得到若干个周期规律更为清晰的子序列;再利用多尺度熵(multiscale entropy,MSE)计算所有子序列的复杂程度,根据不同时间尺度上的样本熵值将相似的子序列重构聚合;然后,利用SSA去噪的功能,对高度复杂的新序列进行二次分解,去除序列中的噪声并提取更为主要的规律,从而进一步提高中长序列预测精度;再将得到的最终一组子序列输入BiLSTM进行预测;最后,考虑到天气、节假日等外部因素对电力负荷的影响,提出了一种误差修正技术。选取了巴拿马某地区的用电负荷进行实验,实验结果表明,经过双重分解可以将均方根误差降低87.4%;预测未来一年的负荷序列时,采用的BiLSTM模型将拟合系数最高提高2.5%;所提出的误差修正技术可将均方根误差降低9.7%。
文摘跨介质航行体入水瞬间会受到巨大的冲击载荷,极易导致结构破坏甚至内部器件失灵。为发展有效的降载方法,本文基于VOF(Volume of Fluid)多相流模型,研究头部喷气航行体入水过程的载荷特性和流体动力特性,分析喷气压力、喷气高度对降载效果的影响,并探索头部喷气降载方法有效性的入水速度范围。研究结果表明,头部喷气使自由液面下凹形成空腔,并能极大地降低航行体所受阻力和冲击力,延缓了航行体撞水时间,从而实现冲击载荷控制;喷气压力和喷气高度对入水空泡形态及冲击压力峰值的影响都不大,合理的选择既能达到降载效果又能节约喷气量;入水速度为50 m/s时,降载量高达76.51%,但当入水速度为300 m/s时,降载量仅为39.92%。因此,针对高亚声速跨介质入水问题,需进一步探索主被动相结合的复合降载方法。
文摘项目供热面积约1.14×10^(5)m^(2),总热负荷7000 k W,采用中深层地热供热,平均热负荷为4859.09 kW,项目年耗热量为62959.68 GJ。热源为1口水热型地热井,按照“一采一灌、同层回灌、取热不耗水”的模式进行建设,配套回灌井1口。利用中深层地热水作为供热热源,冬季提供45℃/40℃采暖循环水,采用“板换直供+热泵机组调峰”的方式为项目供热。按照30年运行期测算,中深层地热方案相比于天然气锅炉方案可节省费用1.127738×10^(8)元。
