Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devi...Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devices have made power load data increasingly complex and volatile.This places higher demands on the prediction and analysis of power loads.In order to improve the prediction accuracy of short-term power load,a CNN-BiLSTMTPA short-term power prediction model based on the Improved Whale Optimization Algorithm(IWOA)with mixed strategies was proposed.Firstly,the model combined the Convolutional Neural Network(CNN)with the Bidirectional Long Short-Term Memory Network(BiLSTM)to fully extract the spatio-temporal characteristics of the load data itself.Then,the Temporal Pattern Attention(TPA)mechanism was introduced into the CNN-BiLSTM model to automatically assign corresponding weights to the hidden states of the BiLSTM.This allowed the model to differentiate the importance of load sequences at different time intervals.At the same time,in order to solve the problem of the difficulties of selecting the parameters of the temporal model,and the poor global search ability of the whale algorithm,which is easy to fall into the local optimization,the whale algorithm(IWOA)was optimized by using the hybrid strategy of Tent chaos mapping and Levy flight strategy,so as to better search the parameters of the model.In this experiment,the real load data of a region in Zhejiang was taken as an example to analyze,and the prediction accuracy(R2)of the proposed method reached 98.83%.Compared with the prediction models such as BP,WOA-CNN-BiLSTM,SSA-CNN-BiLSTM,CNN-BiGRU-Attention,etc.,the experimental results showed that the model proposed in this study has a higher prediction accuracy.展开更多
According to the chaotic and non-linear characters of power load data,the time series matrix is established with the theory of phase-space reconstruction,and then Lyapunov exponents with chaotic time series are comput...According to the chaotic and non-linear characters of power load data,the time series matrix is established with the theory of phase-space reconstruction,and then Lyapunov exponents with chaotic time series are computed to determine the time delay and the embedding dimension.Due to different features of the data,data mining algorithm is conducted to classify the data into different groups.Redundant information is eliminated by the advantage of data mining technology,and the historical loads that have highly similar features with the forecasting day are searched by the system.As a result,the training data can be decreased and the computing speed can also be improved when constructing support vector machine(SVM) model.Then,SVM algorithm is used to predict power load with parameters that get in pretreatment.In order to prove the effectiveness of the new model,the calculation with data mining SVM algorithm is compared with that of single SVM and back propagation network.It can be seen that the new DSVM algorithm effectively improves the forecast accuracy by 0.75%,1.10% and 1.73% compared with SVM for two random dimensions of 11-dimension,14-dimension and BP network,respectively.This indicates that the DSVM gains perfect improvement effect in the short-term power load forecasting.展开更多
Short-term forecasting is a difficult problem because of the influence of non-linear factors and irregular events.A novel short-term forecasting method named TIK was proposed,in which ARMA forecasting model was used t...Short-term forecasting is a difficult problem because of the influence of non-linear factors and irregular events.A novel short-term forecasting method named TIK was proposed,in which ARMA forecasting model was used to consider the load time series trend forecasting,intelligence forecasting DESVR model was applied to estimate the non-linear influence,and knowledge mining methods were applied to correct the errors caused by irregular events.In order to prove the effectiveness of the proposed model,an application of the daily maximum load forecasting was evaluated.The experimental results show that the DESVR model improves the mean absolute percentage error(MAPE) from 2.82% to 2.55%,and the knowledge rules can improve the MAPE from 2.55% to 2.30%.Compared with the single ARMA forecasting method and ARMA combined SVR forecasting method,it can be proved that TIK method gains the best performance in short-term load forecasting.展开更多
During the Twelfth Five-Year plan,large-scale construction of smart grid with safe and stable operation requires a timely and accurate short-term load forecasting method.Moreover,along with the full-scale smart grid c...During the Twelfth Five-Year plan,large-scale construction of smart grid with safe and stable operation requires a timely and accurate short-term load forecasting method.Moreover,along with the full-scale smart grid construction,the power supply mode and consumption mode of the whole system can be optimized through the accurate short-term load forecasting;and the security,stability and cleanness of the system can be guaranteed.展开更多
Power load forecasting accuracy related to the development of the power system. There were so many factors influencing the power load, but their effects were not the same and what factors played a leading role could n...Power load forecasting accuracy related to the development of the power system. There were so many factors influencing the power load, but their effects were not the same and what factors played a leading role could not be determined empirically. Based on the analysis of the principal component, the paper forecasted the demands of power load with the method of the multivariate linear regression model prediction. Took the rural power grid load for example, the paper analyzed the impacts of different factors on power load, selected the forecast methods which were appropriate for using in this area, forecasted its 2014-2018 electricity load, and provided a reliable basis for grid planning.展开更多
Firstly,general regression neural network(GRNN) was used for variable selection of key influencing factors of residential load(RL) forecasting.Secondly,the key influencing factors chosen by GRNN were used as the input...Firstly,general regression neural network(GRNN) was used for variable selection of key influencing factors of residential load(RL) forecasting.Secondly,the key influencing factors chosen by GRNN were used as the input and output terminals of urban and rural RL for simulating and learning.In addition,the suitable parameters of final model were obtained through applying the evidence theory to combine the optimization results which were calculated with the PSO method and the Bayes theory.Then,the model of PSO-Bayes least squares support vector machine(PSO-Bayes-LS-SVM) was established.A case study was then provided for the learning and testing.The empirical analysis results show that the mean square errors of urban and rural RL forecast are 0.02% and 0.04%,respectively.At last,taking a specific province RL in China as an example,the forecast results of RL from 2011 to 2015 were obtained.展开更多
超短期电力负荷预测作为电力系统的基本组成,能为生产调度计划的制定提供重要依据。然而,电力负荷具有非线性、时变性和不确定性,充分挖掘其潜在特征并分别预测,是提升预测准确性的关键。提出一种基于自适应局部迭代滤波(adaptive local...超短期电力负荷预测作为电力系统的基本组成,能为生产调度计划的制定提供重要依据。然而,电力负荷具有非线性、时变性和不确定性,充分挖掘其潜在特征并分别预测,是提升预测准确性的关键。提出一种基于自适应局部迭代滤波(adaptive local iterative filtering,ALIF)的BiGRU-Attention-XGBoost电力负荷组合预测模型。该模型基于ALIF-SE实现将历史负荷序列分解重组为周期序列、波动序列和趋势序列;通过Attention机制对BiGRU模型进行改进,并结合XGBoost模型构建基于时变权重组合的电力负荷预测模型。实验分析表明,输入模型数据经过ALIF-SE处理后预测精度有明显提升;所提组合模型在工作日和节假日均具有较好的预测效果,预测误差大部分在5%以下;通过在不同负荷数据集下进行实验对比,验证了所提预测方法的可迁移性。实验结果证明,所提模型具有有效性、准确性和可行性。展开更多
高强度开采和工作面长度增加使得矿压显现规律和时空分布特征出现变化,实现顶板来压的智能预测对于保障矿井安全生产具有重要意义。以陕西榆横矿区袁大滩煤矿中厚煤层加长工作面高强度开采的矿压演化趋势和分级预测为背景,分析了加长工...高强度开采和工作面长度增加使得矿压显现规律和时空分布特征出现变化,实现顶板来压的智能预测对于保障矿井安全生产具有重要意义。以陕西榆横矿区袁大滩煤矿中厚煤层加长工作面高强度开采的矿压演化趋势和分级预测为背景,分析了加长工作面支架阻力的分布特征和矿压显现规律,将工作面矿压数据动态映射至具有拓扑关系的空间网格单元,利用无监督聚类算法提取了工作面支架时空关联特征,形成了时空联动的支架阻力分析方法,构建了基于patch机制的Transformer(Patch Time Series Transformer,PatchTST)矿压预测模型,基于现场实测数据横向对比测试了多种预测模型,验证了PatchTST的准确性和对矿压长序列预测的适用性,最后进行了工程应用性能测试和预测误差分析。结果表明:袁大滩煤矿11207加长工作面倾向方向压力分布呈现“双波峰−谷间震荡”的“M”型特征,随着推进度和时间推移,“M”型压力场总体呈现出“形成−稳定−递归”的周期性演化规律;矿压数据经过空间网格单元的动态映射和聚类分析后,可以精确辨识工作面来压积聚区域并实现来压强度分级的自动求解;PatchTST模型在回视窗口240,预测步长为3的情况下预测精度最佳,评估指标M_(SE)值和M_(AE)值分别为0.095、0.240;横向对比多个基于注意力机制的模型,PatchTST模型均能做到最低的预测误差;工程应用性能测试表明,所用方法准确辨识了现场观测较为强烈的来压,误差分析同样表明模型的预测精度较高,准确率可达92.8%。研究可为加长工作面矿压显现规律及工作面来压的智能预测预警提供借鉴与参考。展开更多
基于数据驱动的方法已广泛应用于电力负荷预测领域,以提升预测精度。然而,当售电公司接入新用户时,由于缺乏用户历史用电数据,常规数据驱动方法的适用性会受到一定限制。为解决这一问题,文章提出了一种基于域对抗迁移网络(domain advers...基于数据驱动的方法已广泛应用于电力负荷预测领域,以提升预测精度。然而,当售电公司接入新用户时,由于缺乏用户历史用电数据,常规数据驱动方法的适用性会受到一定限制。为解决这一问题,文章提出了一种基于域对抗迁移网络(domain adversarial transfer network,DATN)的短期电力负荷预测方法。该模型利用Transformer模型作为特征提取器,以捕捉负荷数据中的动态特征和时间依赖性。随后,负荷预测器基于这些特征精准预测未来的负荷情况。通过域判别器与特征提取器的对抗学习,确保模型能够学习到深层域不变特征,同时结合多核最大均值差异(multi-kernel maximum mean discrepancy,MK-MMD)和相关性对齐(correlation alignment,CORAL)进一步减小源域与目标域数据的分布差异。所提模型在南方某省工业用户的用电数据上进行了验证,实验结果表明,在小样本场景下,该方法具备较好的预测精度和场景适应性。展开更多
为解决深度学习预测模型在数据不足时准确性受限的问题,提出一种结合Transformer的交叉注意力(cross-attention in Transformer,CATrans)机制和域分离网络(domain separation networks,DSN)的深度迁移学习方法——CATrans-DSN,用于短期...为解决深度学习预测模型在数据不足时准确性受限的问题,提出一种结合Transformer的交叉注意力(cross-attention in Transformer,CATrans)机制和域分离网络(domain separation networks,DSN)的深度迁移学习方法——CATrans-DSN,用于短期跨建筑负荷预测。CATrans特征提取器利用注意力机制来学习源域和目标域负荷数据的域共有和私有时间特征,并利用共有特征进行知识迁移;特征重构器作为辅助模块,对源域和目标域数据进行数据重构;由回归预测器将学习到的特征转化为预测值。最后,利用在源域和目标域上训练得到的建筑负荷预测模型,直接用于目标建筑的负荷预测。实验结果表明,所提出的方法有效地提高了数据稀缺情况下的预测准确性和模型泛化能力。展开更多
负荷预测是综合能源系统(integrated energy system,IES)高效运行的前提,面对综合能源系统多元负荷强耦合相关性、强随机性的特点,单一模型在运行负荷特征提取方面存在不足。为充分利用负荷间的相关性、降低负荷数据的非平稳性、弥补单...负荷预测是综合能源系统(integrated energy system,IES)高效运行的前提,面对综合能源系统多元负荷强耦合相关性、强随机性的特点,单一模型在运行负荷特征提取方面存在不足。为充分利用负荷间的相关性、降低负荷数据的非平稳性、弥补单一模型的不足,提出一种基于TCN-TPABiLSTM组合模型和多任务学习框架的IES多元负荷超短期协同预测方法。首先对负荷间耦合相关性、负荷时间相关性和负荷影响因素进行分析以构建模型输入,再通过变分模态分解将负荷数据分解为一定数量的模态以降低非平稳性,最后以TCN-TPA-BiLSTM组合模型作为多任务学习框架的共享层进行预测。通过实际数据进行验证和对比,结果表明该方法能够充分发挥模型各部分优势,相较于其他模型也获得了更优的结果。展开更多
文摘Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devices have made power load data increasingly complex and volatile.This places higher demands on the prediction and analysis of power loads.In order to improve the prediction accuracy of short-term power load,a CNN-BiLSTMTPA short-term power prediction model based on the Improved Whale Optimization Algorithm(IWOA)with mixed strategies was proposed.Firstly,the model combined the Convolutional Neural Network(CNN)with the Bidirectional Long Short-Term Memory Network(BiLSTM)to fully extract the spatio-temporal characteristics of the load data itself.Then,the Temporal Pattern Attention(TPA)mechanism was introduced into the CNN-BiLSTM model to automatically assign corresponding weights to the hidden states of the BiLSTM.This allowed the model to differentiate the importance of load sequences at different time intervals.At the same time,in order to solve the problem of the difficulties of selecting the parameters of the temporal model,and the poor global search ability of the whale algorithm,which is easy to fall into the local optimization,the whale algorithm(IWOA)was optimized by using the hybrid strategy of Tent chaos mapping and Levy flight strategy,so as to better search the parameters of the model.In this experiment,the real load data of a region in Zhejiang was taken as an example to analyze,and the prediction accuracy(R2)of the proposed method reached 98.83%.Compared with the prediction models such as BP,WOA-CNN-BiLSTM,SSA-CNN-BiLSTM,CNN-BiGRU-Attention,etc.,the experimental results showed that the model proposed in this study has a higher prediction accuracy.
基金Project(70671039) supported by the National Natural Science Foundation of China
文摘According to the chaotic and non-linear characters of power load data,the time series matrix is established with the theory of phase-space reconstruction,and then Lyapunov exponents with chaotic time series are computed to determine the time delay and the embedding dimension.Due to different features of the data,data mining algorithm is conducted to classify the data into different groups.Redundant information is eliminated by the advantage of data mining technology,and the historical loads that have highly similar features with the forecasting day are searched by the system.As a result,the training data can be decreased and the computing speed can also be improved when constructing support vector machine(SVM) model.Then,SVM algorithm is used to predict power load with parameters that get in pretreatment.In order to prove the effectiveness of the new model,the calculation with data mining SVM algorithm is compared with that of single SVM and back propagation network.It can be seen that the new DSVM algorithm effectively improves the forecast accuracy by 0.75%,1.10% and 1.73% compared with SVM for two random dimensions of 11-dimension,14-dimension and BP network,respectively.This indicates that the DSVM gains perfect improvement effect in the short-term power load forecasting.
基金Projects(70671039,71071052) supported by the National Natural Science Foundation of ChinaProjects(10QX44,09QX68) supported by the Fundamental Research Funds for the Central Universities in China
文摘Short-term forecasting is a difficult problem because of the influence of non-linear factors and irregular events.A novel short-term forecasting method named TIK was proposed,in which ARMA forecasting model was used to consider the load time series trend forecasting,intelligence forecasting DESVR model was applied to estimate the non-linear influence,and knowledge mining methods were applied to correct the errors caused by irregular events.In order to prove the effectiveness of the proposed model,an application of the daily maximum load forecasting was evaluated.The experimental results show that the DESVR model improves the mean absolute percentage error(MAPE) from 2.82% to 2.55%,and the knowledge rules can improve the MAPE from 2.55% to 2.30%.Compared with the single ARMA forecasting method and ARMA combined SVR forecasting method,it can be proved that TIK method gains the best performance in short-term load forecasting.
文摘During the Twelfth Five-Year plan,large-scale construction of smart grid with safe and stable operation requires a timely and accurate short-term load forecasting method.Moreover,along with the full-scale smart grid construction,the power supply mode and consumption mode of the whole system can be optimized through the accurate short-term load forecasting;and the security,stability and cleanness of the system can be guaranteed.
基金Supported by the Science and Technology Research Project Fund of Provincial Department of Education(12531004)Project of Heilongjiang Leading Talent Echelon Talented(2012)
文摘Power load forecasting accuracy related to the development of the power system. There were so many factors influencing the power load, but their effects were not the same and what factors played a leading role could not be determined empirically. Based on the analysis of the principal component, the paper forecasted the demands of power load with the method of the multivariate linear regression model prediction. Took the rural power grid load for example, the paper analyzed the impacts of different factors on power load, selected the forecast methods which were appropriate for using in this area, forecasted its 2014-2018 electricity load, and provided a reliable basis for grid planning.
基金Project(07JA790092) supported by the Research Grants from Humanities and Social Science Program of Ministry of Education of ChinaProject(10MR44) supported by the Fundamental Research Funds for the Central Universities in China
文摘Firstly,general regression neural network(GRNN) was used for variable selection of key influencing factors of residential load(RL) forecasting.Secondly,the key influencing factors chosen by GRNN were used as the input and output terminals of urban and rural RL for simulating and learning.In addition,the suitable parameters of final model were obtained through applying the evidence theory to combine the optimization results which were calculated with the PSO method and the Bayes theory.Then,the model of PSO-Bayes least squares support vector machine(PSO-Bayes-LS-SVM) was established.A case study was then provided for the learning and testing.The empirical analysis results show that the mean square errors of urban and rural RL forecast are 0.02% and 0.04%,respectively.At last,taking a specific province RL in China as an example,the forecast results of RL from 2011 to 2015 were obtained.
文摘超短期电力负荷预测作为电力系统的基本组成,能为生产调度计划的制定提供重要依据。然而,电力负荷具有非线性、时变性和不确定性,充分挖掘其潜在特征并分别预测,是提升预测准确性的关键。提出一种基于自适应局部迭代滤波(adaptive local iterative filtering,ALIF)的BiGRU-Attention-XGBoost电力负荷组合预测模型。该模型基于ALIF-SE实现将历史负荷序列分解重组为周期序列、波动序列和趋势序列;通过Attention机制对BiGRU模型进行改进,并结合XGBoost模型构建基于时变权重组合的电力负荷预测模型。实验分析表明,输入模型数据经过ALIF-SE处理后预测精度有明显提升;所提组合模型在工作日和节假日均具有较好的预测效果,预测误差大部分在5%以下;通过在不同负荷数据集下进行实验对比,验证了所提预测方法的可迁移性。实验结果证明,所提模型具有有效性、准确性和可行性。
文摘高强度开采和工作面长度增加使得矿压显现规律和时空分布特征出现变化,实现顶板来压的智能预测对于保障矿井安全生产具有重要意义。以陕西榆横矿区袁大滩煤矿中厚煤层加长工作面高强度开采的矿压演化趋势和分级预测为背景,分析了加长工作面支架阻力的分布特征和矿压显现规律,将工作面矿压数据动态映射至具有拓扑关系的空间网格单元,利用无监督聚类算法提取了工作面支架时空关联特征,形成了时空联动的支架阻力分析方法,构建了基于patch机制的Transformer(Patch Time Series Transformer,PatchTST)矿压预测模型,基于现场实测数据横向对比测试了多种预测模型,验证了PatchTST的准确性和对矿压长序列预测的适用性,最后进行了工程应用性能测试和预测误差分析。结果表明:袁大滩煤矿11207加长工作面倾向方向压力分布呈现“双波峰−谷间震荡”的“M”型特征,随着推进度和时间推移,“M”型压力场总体呈现出“形成−稳定−递归”的周期性演化规律;矿压数据经过空间网格单元的动态映射和聚类分析后,可以精确辨识工作面来压积聚区域并实现来压强度分级的自动求解;PatchTST模型在回视窗口240,预测步长为3的情况下预测精度最佳,评估指标M_(SE)值和M_(AE)值分别为0.095、0.240;横向对比多个基于注意力机制的模型,PatchTST模型均能做到最低的预测误差;工程应用性能测试表明,所用方法准确辨识了现场观测较为强烈的来压,误差分析同样表明模型的预测精度较高,准确率可达92.8%。研究可为加长工作面矿压显现规律及工作面来压的智能预测预警提供借鉴与参考。
文摘基于数据驱动的方法已广泛应用于电力负荷预测领域,以提升预测精度。然而,当售电公司接入新用户时,由于缺乏用户历史用电数据,常规数据驱动方法的适用性会受到一定限制。为解决这一问题,文章提出了一种基于域对抗迁移网络(domain adversarial transfer network,DATN)的短期电力负荷预测方法。该模型利用Transformer模型作为特征提取器,以捕捉负荷数据中的动态特征和时间依赖性。随后,负荷预测器基于这些特征精准预测未来的负荷情况。通过域判别器与特征提取器的对抗学习,确保模型能够学习到深层域不变特征,同时结合多核最大均值差异(multi-kernel maximum mean discrepancy,MK-MMD)和相关性对齐(correlation alignment,CORAL)进一步减小源域与目标域数据的分布差异。所提模型在南方某省工业用户的用电数据上进行了验证,实验结果表明,在小样本场景下,该方法具备较好的预测精度和场景适应性。
文摘为解决深度学习预测模型在数据不足时准确性受限的问题,提出一种结合Transformer的交叉注意力(cross-attention in Transformer,CATrans)机制和域分离网络(domain separation networks,DSN)的深度迁移学习方法——CATrans-DSN,用于短期跨建筑负荷预测。CATrans特征提取器利用注意力机制来学习源域和目标域负荷数据的域共有和私有时间特征,并利用共有特征进行知识迁移;特征重构器作为辅助模块,对源域和目标域数据进行数据重构;由回归预测器将学习到的特征转化为预测值。最后,利用在源域和目标域上训练得到的建筑负荷预测模型,直接用于目标建筑的负荷预测。实验结果表明,所提出的方法有效地提高了数据稀缺情况下的预测准确性和模型泛化能力。
文摘负荷预测是综合能源系统(integrated energy system,IES)高效运行的前提,面对综合能源系统多元负荷强耦合相关性、强随机性的特点,单一模型在运行负荷特征提取方面存在不足。为充分利用负荷间的相关性、降低负荷数据的非平稳性、弥补单一模型的不足,提出一种基于TCN-TPABiLSTM组合模型和多任务学习框架的IES多元负荷超短期协同预测方法。首先对负荷间耦合相关性、负荷时间相关性和负荷影响因素进行分析以构建模型输入,再通过变分模态分解将负荷数据分解为一定数量的模态以降低非平稳性,最后以TCN-TPA-BiLSTM组合模型作为多任务学习框架的共享层进行预测。通过实际数据进行验证和对比,结果表明该方法能够充分发挥模型各部分优势,相较于其他模型也获得了更优的结果。
