Crop coverage(CC)is an important parameter to represent crop growth characteristics,and the ahead forecasting of CC is helpful to track crop growth trends and guide agricultural management decisions.In this study,a no...Crop coverage(CC)is an important parameter to represent crop growth characteristics,and the ahead forecasting of CC is helpful to track crop growth trends and guide agricultural management decisions.In this study,a novel CNN-LSTM model that combined the advantages of convolutional neural network(CNN)in feature extraction and long short-term memory(LSTM)in time series processing was proposed for multi-day ahead forecasting of maize CC.Considering the influence of climate change on maize growth,five microclimatic factors were combined with historical maize CC estimated from field images as the input variables of the forecasting model.The field experimental data of four observation points for more than three years were used to evaluate the performance of CNN-LSTM at the forecasting horizon of three to seven days ahead and compared the forecasting results to CNN and LSTM.The results demonstrated that CNN-LSTM obtained the lowest RMSE and the highest R2 at all forecasting horizons.Subsequently,the performance of CNN-LSTM under univariate(historical maize CC)and multivariate(historical maize CC+microclimatic factors)input was compared,and the results indicated that additional microclimatic factors were effective in improving the forecasting performance.Furthermore,the 3-day ahead forecasting results of CNN-LSTM in different growth stages of maize were also analyzed,and the results showed that the highest forecasting accuracy was obtained in the seven leaves stage.Therefore,CNN-LSTM can be considered a useful tool to forecast maize CC.展开更多
A new methodology for multi-step-ahead forecasting was proposed herein which combined the wavelet transform(WT), artificial neural network(ANN) and forecasting strategies based on the changing characteristics of avail...A new methodology for multi-step-ahead forecasting was proposed herein which combined the wavelet transform(WT), artificial neural network(ANN) and forecasting strategies based on the changing characteristics of available parking spaces(APS). First, several APS time series were decomposed and reconstituted by the wavelet transform. Then, using an artificial neural network, the following five strategies for multi-step-ahead time series forecasting were used to forecast the reconstructed time series: recursive strategy, direct strategy, multi-input multi-output(MIMO) strategy, DIRMO strategy(a combination of the direct and MIMO strategies), and newly proposed recursive multi-input multi-output(RECMO) strategy which is a combination of the recursive and MIMO strategies. Finally, integrating the predicted results with the reconstructed time series produced the final forecasted available parking spaces. Three findings appear to be consistently supported by the experimental results. First, applying the wavelet transform to multi-step ahead available parking spaces forecasting can effectively improve the forecasting accuracy. Second, the forecasting resulted from the DIRMO and RECMO strategies is more accurate than that of the other strategies. Finally, the RECMO strategy requires less model training time than the DIRMO strategy and consumes the least amount of training time among five forecasting strategies.展开更多
现有电力负荷预测方法面临诸多挑战,尤其是在考虑气象因素对负荷波动的影响时,传统方法往往忽视气象特征与负荷之间复杂的非线性关系,导致预测精度不足。对此文中提出一种基于气象相似日修正(meteorological similar day correction,MS...现有电力负荷预测方法面临诸多挑战,尤其是在考虑气象因素对负荷波动的影响时,传统方法往往忽视气象特征与负荷之间复杂的非线性关系,导致预测精度不足。对此文中提出一种基于气象相似日修正(meteorological similar day correction,MSDC)和改进鹦鹉优化(improved parrot optimizer,IPO)线性分解(decomposition-based linear,DLinear)的日前电力负荷预测模型。首先运用Logistic映射、自适应变异策略、螺旋波动搜索IPO对DLinear超参数进行优化,然后由DLinear提取数据的周期性和趋势性特征,最后通过比对气象特征欧氏距离修正负荷预测值,形成基于IPO-DLinear-MSDC的日前电力负荷预测模型。采用2024年6月至10月湖南株洲地区总电力负荷数据集进行仿真分析,IPO-DLinear-MSDC模型的输出平均绝对百分比误差(mean absolute percentage error,MAPE)、决定系数R2分别为4.67%、0.833,相较于IPO-DLinear与PO-DLinear模型,MAPE分别下降了0.83个百分点、1.43个百分点,R2分别提升了0.074、0.125。展开更多
Management and efficient operations in critical infrastructures such as smart grids take huge advantage of accurate power load forecasting,which,due to its non-linear nature,remains a challenging task.Recently,deep le...Management and efficient operations in critical infrastructures such as smart grids take huge advantage of accurate power load forecasting,which,due to its non-linear nature,remains a challenging task.Recently,deep learning has emerged in the machine learning field achieving impressive performance in a vast range of tasks,from image classification to machine translation.Applications of deep learning models to the electric load forecasting problem are gaining interest among researchers as well as the industry,but a comprehensive and sound comparison among different-also traditional-architectures is not yet available in the literature.This work aims at filling the gap by reviewing and experimentally evaluating four real world datasets on the most recent trends in electric load forecasting,by contrasting deep learning architectures on short-term forecast(oneday-ahead prediction).Specifically,the focus is on feedforward and recurrent neural networks,sequence-to-sequence models and temporal convolutional neural networks along with architectural variants,which are known in the signal processing community but are novel to the load forecasting one.展开更多
径流过程呈现出的强非线性,使得现有水文模型的预测性能受到制约,深度学习等人工智能方法具有较强的非线性拟合能力,一定程度上可以突破现有瓶颈。为有效提取径流序列的非线性时变特征信息,提高径流模拟精度和多步预测性能,以雅砻江上...径流过程呈现出的强非线性,使得现有水文模型的预测性能受到制约,深度学习等人工智能方法具有较强的非线性拟合能力,一定程度上可以突破现有瓶颈。为有效提取径流序列的非线性时变特征信息,提高径流模拟精度和多步预测性能,以雅砻江上游雅江流域为研究对象,建立了基于具有时变结构的ForecastNet径流预测模型,并与传统水文模型SWAT(Soil and Water Assessnent Teol)和神经网络模型RNN(Recurrent Neural Network)、LSTM(Long Short-Term Memory)及其组合进行对比分析。结果表明,ForcastNet模型在长预见期径流预测中有较强的适用性,能有效提高径流模拟及多步预测精度,为高精度实时径流预测提供了一种技术支撑。展开更多
In this study,we address a demanding time series forecasting problem that deals simultaneously with the following:(1)intermittent time series,(2)multi-step ahead forecasting,(3)time series with multiple seasonal perio...In this study,we address a demanding time series forecasting problem that deals simultaneously with the following:(1)intermittent time series,(2)multi-step ahead forecasting,(3)time series with multiple seasonal periods,and(4)performance measures for model selection across multiple time series.Current literature deals with these types of problems separately,and no study has dealt with all these characteristics simultaneously.To fill this knowledge gap,we begin by reviewing all the necessary existing literature relevant to this case study with the goal of proposing a framework capable of achieving adequate forecast accuracy for such a complex problem.Several adaptions and innovations have been conducted,which are marked as contributions to the literature.Specifically,we proposed a weighted average forecast combination of many cutting-edge models based on their out-of-sample performance.To gather strong evidence that our ensemble model works in practice,we undertook a large-scale study across 98 time series,rigorously assessed with unbiased performance measures,where a week seasonal naïve was set as a benchmark.The results demonstrate that the proposed ensemble model achieves eyecatching forecasting accuracy.展开更多
In recent years, introduction of a renewable energy source such as solar energy is expected. However, solar radiation is not constant and power output of photovoltaic (PV) system is influenced by weather conditions. I...In recent years, introduction of a renewable energy source such as solar energy is expected. However, solar radiation is not constant and power output of photovoltaic (PV) system is influenced by weather conditions. It is difficult for getting to know accurate power output of PV system. In order to forecast the power output of PV system as accurate as possible, this paper proposes a decision technique of forecasting model for short-term-ahead power output of PV system based on solar radiation prediction. Application of Recurrent Neural Network (RNN) is shown for solar radiation prediction in this paper. The proposed method in this paper does not require complicated calculation, but mathematical model with only useful weather data. The validity of the proposed RNN is confirmed by comparing simulation results of solar radiation forecasting with that obtained from other展开更多
In recent years, there has been introduction of alternative energy sources such as wind energy. However, wind speed is not constant and wind power output is proportional to the cube of the wind speed. In order to cont...In recent years, there has been introduction of alternative energy sources such as wind energy. However, wind speed is not constant and wind power output is proportional to the cube of the wind speed. In order to control the power output for wind power generators as accurately as possible, a method of wind speed estimation is required. In this paper, a technique considers that wind speed in the order of 1 - 30 seconds is investigated in confirming the validity of the Auto Regressive model (AR), Kalman Filter (KF) and Neural Network (NN) to forecast wind speed. This paper compares the simulation results of the forecast wind speed for the power output forecast of wind power generator by using AR, KF and NN.展开更多
为探寻地区电网调度分布式光伏发电量预测的实用方法和提高预测的准确性,提出了一种结合数值天气预报和历史数据统计模型的地区分布式光伏发电量日前预测方法。首先,利用机器人流程自动化(robot process automation, RPA)技术和气象服...为探寻地区电网调度分布式光伏发电量预测的实用方法和提高预测的准确性,提出了一种结合数值天气预报和历史数据统计模型的地区分布式光伏发电量日前预测方法。首先,利用机器人流程自动化(robot process automation, RPA)技术和气象服务平台,自动获取光伏发电数据和天气预报信息。其次,通过分析各县域历史发电数据与天气因素之间的关系,建立预测模型,该模型考虑了辐射温度、湿度和风速等主要影响因素,通过回归分析方法进行建立和验证。最后,仿真分析结果表明,该方法相比传统方法有显著改进,能有效提高预测的准确性和可靠性,自动化的数据收集与处理流程不仅提升了工作效率,还降低了人为错误率。该方法对于电网调度优化发电计划,提高可再生能源利用率具有一定的实用价值。展开更多
为了深度挖掘电价序列中所蕴含的特征与信息,进一步提升日前电价的预测准确率,提出一种基于改进互信息特征选取(improve mutual information feature selection,IMIFS)、变分模态分解(variational mode decomposition,VMD)和红鸢优化算...为了深度挖掘电价序列中所蕴含的特征与信息,进一步提升日前电价的预测准确率,提出一种基于改进互信息特征选取(improve mutual information feature selection,IMIFS)、变分模态分解(variational mode decomposition,VMD)和红鸢优化算法(red kite optimization algorithm,ROA)优化长短记忆网络(long short term memory,LSTM)相结合的混合日前电价预测模型。首先,通过IMIFS对原始多元特征集进行降维,提取出包含维度最小且电价信息丰富的特征集,同时,利用VMD对电价序列进行有效分解,减轻电价序列的波动性;其次,引入ROA对LSTM中阈值与权重进行优化,提升算法的全局搜索与局部寻优能力;最后,通过算例验证IMIFS-VMD和ROA-LSTM日前电价预测模型效果,结果表明所提模型X_(RMSE)、X_(MAE)和R^(2)分别为2.532元/(MW·h)、1.956元/(MW·h)和98.06%,较其他电价预测模型具有较高的预测准确率。展开更多
基金financially supported by the National Natural Science Foundation of China(Grant No.61772240No.51961125102)the 111 Project(B12018).
文摘Crop coverage(CC)is an important parameter to represent crop growth characteristics,and the ahead forecasting of CC is helpful to track crop growth trends and guide agricultural management decisions.In this study,a novel CNN-LSTM model that combined the advantages of convolutional neural network(CNN)in feature extraction and long short-term memory(LSTM)in time series processing was proposed for multi-day ahead forecasting of maize CC.Considering the influence of climate change on maize growth,five microclimatic factors were combined with historical maize CC estimated from field images as the input variables of the forecasting model.The field experimental data of four observation points for more than three years were used to evaluate the performance of CNN-LSTM at the forecasting horizon of three to seven days ahead and compared the forecasting results to CNN and LSTM.The results demonstrated that CNN-LSTM obtained the lowest RMSE and the highest R2 at all forecasting horizons.Subsequently,the performance of CNN-LSTM under univariate(historical maize CC)and multivariate(historical maize CC+microclimatic factors)input was compared,and the results indicated that additional microclimatic factors were effective in improving the forecasting performance.Furthermore,the 3-day ahead forecasting results of CNN-LSTM in different growth stages of maize were also analyzed,and the results showed that the highest forecasting accuracy was obtained in the seven leaves stage.Therefore,CNN-LSTM can be considered a useful tool to forecast maize CC.
基金Project(51561135003)supported by the International Cooperation and Exchange of the National Natural Science Foundation of ChinaProject(51338003)supported by the Key Project of National Natural Science Foundation of China
文摘A new methodology for multi-step-ahead forecasting was proposed herein which combined the wavelet transform(WT), artificial neural network(ANN) and forecasting strategies based on the changing characteristics of available parking spaces(APS). First, several APS time series were decomposed and reconstituted by the wavelet transform. Then, using an artificial neural network, the following five strategies for multi-step-ahead time series forecasting were used to forecast the reconstructed time series: recursive strategy, direct strategy, multi-input multi-output(MIMO) strategy, DIRMO strategy(a combination of the direct and MIMO strategies), and newly proposed recursive multi-input multi-output(RECMO) strategy which is a combination of the recursive and MIMO strategies. Finally, integrating the predicted results with the reconstructed time series produced the final forecasted available parking spaces. Three findings appear to be consistently supported by the experimental results. First, applying the wavelet transform to multi-step ahead available parking spaces forecasting can effectively improve the forecasting accuracy. Second, the forecasting resulted from the DIRMO and RECMO strategies is more accurate than that of the other strategies. Finally, the RECMO strategy requires less model training time than the DIRMO strategy and consumes the least amount of training time among five forecasting strategies.
文摘现有电力负荷预测方法面临诸多挑战,尤其是在考虑气象因素对负荷波动的影响时,传统方法往往忽视气象特征与负荷之间复杂的非线性关系,导致预测精度不足。对此文中提出一种基于气象相似日修正(meteorological similar day correction,MSDC)和改进鹦鹉优化(improved parrot optimizer,IPO)线性分解(decomposition-based linear,DLinear)的日前电力负荷预测模型。首先运用Logistic映射、自适应变异策略、螺旋波动搜索IPO对DLinear超参数进行优化,然后由DLinear提取数据的周期性和趋势性特征,最后通过比对气象特征欧氏距离修正负荷预测值,形成基于IPO-DLinear-MSDC的日前电力负荷预测模型。采用2024年6月至10月湖南株洲地区总电力负荷数据集进行仿真分析,IPO-DLinear-MSDC模型的输出平均绝对百分比误差(mean absolute percentage error,MAPE)、决定系数R2分别为4.67%、0.833,相较于IPO-DLinear与PO-DLinear模型,MAPE分别下降了0.83个百分点、1.43个百分点,R2分别提升了0.074、0.125。
基金Innosuisse-Schweizerische Agentur für Innovationsförderung,Grant/Award Number:1155002544。
文摘Management and efficient operations in critical infrastructures such as smart grids take huge advantage of accurate power load forecasting,which,due to its non-linear nature,remains a challenging task.Recently,deep learning has emerged in the machine learning field achieving impressive performance in a vast range of tasks,from image classification to machine translation.Applications of deep learning models to the electric load forecasting problem are gaining interest among researchers as well as the industry,but a comprehensive and sound comparison among different-also traditional-architectures is not yet available in the literature.This work aims at filling the gap by reviewing and experimentally evaluating four real world datasets on the most recent trends in electric load forecasting,by contrasting deep learning architectures on short-term forecast(oneday-ahead prediction).Specifically,the focus is on feedforward and recurrent neural networks,sequence-to-sequence models and temporal convolutional neural networks along with architectural variants,which are known in the signal processing community but are novel to the load forecasting one.
文摘径流过程呈现出的强非线性,使得现有水文模型的预测性能受到制约,深度学习等人工智能方法具有较强的非线性拟合能力,一定程度上可以突破现有瓶颈。为有效提取径流序列的非线性时变特征信息,提高径流模拟精度和多步预测性能,以雅砻江上游雅江流域为研究对象,建立了基于具有时变结构的ForecastNet径流预测模型,并与传统水文模型SWAT(Soil and Water Assessnent Teol)和神经网络模型RNN(Recurrent Neural Network)、LSTM(Long Short-Term Memory)及其组合进行对比分析。结果表明,ForcastNet模型在长预见期径流预测中有较强的适用性,能有效提高径流模拟及多步预测精度,为高精度实时径流预测提供了一种技术支撑。
基金supported by COMPETE:POCI-01-0247-FEDER-039719 and FCT-Fundação para a Ciência e Tecnologia within the Project Scope:UIDB/00127/2020.
文摘In this study,we address a demanding time series forecasting problem that deals simultaneously with the following:(1)intermittent time series,(2)multi-step ahead forecasting,(3)time series with multiple seasonal periods,and(4)performance measures for model selection across multiple time series.Current literature deals with these types of problems separately,and no study has dealt with all these characteristics simultaneously.To fill this knowledge gap,we begin by reviewing all the necessary existing literature relevant to this case study with the goal of proposing a framework capable of achieving adequate forecast accuracy for such a complex problem.Several adaptions and innovations have been conducted,which are marked as contributions to the literature.Specifically,we proposed a weighted average forecast combination of many cutting-edge models based on their out-of-sample performance.To gather strong evidence that our ensemble model works in practice,we undertook a large-scale study across 98 time series,rigorously assessed with unbiased performance measures,where a week seasonal naïve was set as a benchmark.The results demonstrate that the proposed ensemble model achieves eyecatching forecasting accuracy.
文摘In recent years, introduction of a renewable energy source such as solar energy is expected. However, solar radiation is not constant and power output of photovoltaic (PV) system is influenced by weather conditions. It is difficult for getting to know accurate power output of PV system. In order to forecast the power output of PV system as accurate as possible, this paper proposes a decision technique of forecasting model for short-term-ahead power output of PV system based on solar radiation prediction. Application of Recurrent Neural Network (RNN) is shown for solar radiation prediction in this paper. The proposed method in this paper does not require complicated calculation, but mathematical model with only useful weather data. The validity of the proposed RNN is confirmed by comparing simulation results of solar radiation forecasting with that obtained from other
文摘In recent years, there has been introduction of alternative energy sources such as wind energy. However, wind speed is not constant and wind power output is proportional to the cube of the wind speed. In order to control the power output for wind power generators as accurately as possible, a method of wind speed estimation is required. In this paper, a technique considers that wind speed in the order of 1 - 30 seconds is investigated in confirming the validity of the Auto Regressive model (AR), Kalman Filter (KF) and Neural Network (NN) to forecast wind speed. This paper compares the simulation results of the forecast wind speed for the power output forecast of wind power generator by using AR, KF and NN.
文摘为探寻地区电网调度分布式光伏发电量预测的实用方法和提高预测的准确性,提出了一种结合数值天气预报和历史数据统计模型的地区分布式光伏发电量日前预测方法。首先,利用机器人流程自动化(robot process automation, RPA)技术和气象服务平台,自动获取光伏发电数据和天气预报信息。其次,通过分析各县域历史发电数据与天气因素之间的关系,建立预测模型,该模型考虑了辐射温度、湿度和风速等主要影响因素,通过回归分析方法进行建立和验证。最后,仿真分析结果表明,该方法相比传统方法有显著改进,能有效提高预测的准确性和可靠性,自动化的数据收集与处理流程不仅提升了工作效率,还降低了人为错误率。该方法对于电网调度优化发电计划,提高可再生能源利用率具有一定的实用价值。
文摘为了深度挖掘电价序列中所蕴含的特征与信息,进一步提升日前电价的预测准确率,提出一种基于改进互信息特征选取(improve mutual information feature selection,IMIFS)、变分模态分解(variational mode decomposition,VMD)和红鸢优化算法(red kite optimization algorithm,ROA)优化长短记忆网络(long short term memory,LSTM)相结合的混合日前电价预测模型。首先,通过IMIFS对原始多元特征集进行降维,提取出包含维度最小且电价信息丰富的特征集,同时,利用VMD对电价序列进行有效分解,减轻电价序列的波动性;其次,引入ROA对LSTM中阈值与权重进行优化,提升算法的全局搜索与局部寻优能力;最后,通过算例验证IMIFS-VMD和ROA-LSTM日前电价预测模型效果,结果表明所提模型X_(RMSE)、X_(MAE)和R^(2)分别为2.532元/(MW·h)、1.956元/(MW·h)和98.06%,较其他电价预测模型具有较高的预测准确率。
文摘日前光伏发电功率预测是电网经济调度的重要依据。针对K均值(K-means)聚类算法初始聚类中心和聚类数目不易确定的问题和传统神经网络训练参数较多、易陷入局部最优等缺陷,构建了DPK-means和极限学习机(extreme learning machine,ELM)的组合预测算法实现日前光伏发电功率的预测模型。首先,采用密度峰值法(density peaks clustering,DPC)对K-means聚类进行优化,解决了Kmeans算法初始聚类中心和聚类数目不易确定的问题。然后,在利用DPK-means算法对历史气象数据样本聚类分析的基础上,建立ELM预测模型实现日前光伏发电功率的预测。经实测数据验证可知,所提出的组合预测算法可得到较好的预测结果,具有较强的实用性。
