基于随机森林的短期电量负荷精准预测方法

可再生能源并入电网后,电能供给量增加,短期电量负荷情况难以预测,无法制定准确的电能分配策略,由此,提出基于随机森林的短期电量负荷精准预测方法研究。深入分析短期电量负荷预测影响因素（气象、时间、电价与随机干扰因素）,选取适当的模型输入变量（历史电量负荷数据、温度数据与日类型）,结合随机森林算法构建短期电量负荷预测模型,并重复确定相似日的选取规则,采用粒子群优化算法寻找预测模型参数最佳值,将样本集输入至模型中,获得精准的短期电量负荷预测结果。实验数据显示:当输入变量数量达到一定值后,应用提出方法获得的短期电量负荷预测时延稳定在0.55s左右,短期电量负荷预测误差几乎为0,充分证实了提出方法应用性能较佳。     

A new hybrid correction method for short-term load forecasting based on ARIMA,SVR and CSA

Accurate load-forecasting problem is a significant and vital issue, especially in the new competitive electricity market. The models that are employed for forecasting purposes would determine how reliable the last forecasted results are. Therefore, this paper proposes a new hybrid correction method based on autoregressive integrated moving average (ARIMA) model, support vector regression (SVR) and cuckoo search algorithm (CSA) to achieve a more reliable forecasting model. The proposed method gets use of the autocorrelation function (ACF) and the partial ACF to search the stationary or non-stationary behaviour of the investigated time series. In the case of non-stationary data, it will be differenced one or more times to become stationary. After that, Akaike information criterion is utilised to find the appropriate ARIMA model such that the linear component of the data would be captured. Therefore, the ARIMA residuals would contain the non-linear components that should be modelled by use of the SVR model. The role of CSA as a successful optimisation algorithm is to find the optimal SVR parameters for more accurate forecasting. Meanwhile, a novel self-adaptive modification method based on CSA is proposed to empower the total search ability of the algorithm effectively. The proposed method is applied to the empirical peak load data of Fars Electrical Power Company in Iran.     

电力系统短期负荷预测方法研究综述

根据负荷预测基本流程,分别对数据预处理、模型选取、模型优化分别进行了总结分析。首先对传统的数据处理方法进行了概述,并简要介绍了新的数据处理方法。其次,将现有的短期负荷预测方法分为经典方法、传统方法和智能方法,综合分析了现有预测方法的应用原理,详细分析和比较预测方法的优点和不足之处,为了提高预测的精度,一些新的方法就因运而生,目的在于提高预测精度和适应相应各种运行条件。再次,总结分析了传统的预测优化模型,并简要介绍了现有的一些新的优化模型,这些新的优化模型计算结果相比于传统的模型精确度较高,分析了新优化模型的优点和不足之处。文章最后对了未来电力系统负荷预测提出了展望,在进行短期负荷预测时应该考虑电力市场、新能源、电动汽车相关因素的影响。     

基于神经网络的电力负荷预测方法研究

电力负荷预测易受到高频、低频和超低频振荡干扰,导致预测准确性不高,提出基于神经网络的电力负荷预测方法。在无线ZigBee组网协议下进行电力负荷传感器信息组网,构建电网负荷数据采集模型并进行模型修正。根据电力负荷数据采集结果,去除高频、低频和超低频振荡干扰因子。进行神经网络样本数据训练,去除冗余数据,输出电网负荷数据集合。对获得的数据集采用神经网络分类器进行分类融合处理,根据电力负荷数据的融合结果实现电力负荷预测。仿真结果表明,采用该方法进行电力负荷预测的准确性较高,预测过程的抗干扰性较好,在电力负荷的实时监测和信息调度中具有很好的应用价值。     

A novel method of short-term load forecasting based on multiwavelet transform and multiple neural networks

This paper aims to develop a load forecasting method for short-term load forecasting based on multiwavelet transform and multiple neural networks. Firstly, a variable weight combination load forecasting model for power load is proposed and discussed. Secondly, the training data are extracted from power load data through multiwavelet transform. Lastly, the obtained data are trained through a variable weight combination model. BP network, RBF network and wavelet neural network are adopted as the training network, and the trained data from three neural networks are input to a three-layer feedforward neural network for the load forecasting. Simulation results show that accuracy of the combination load forecasting model proposed in the paper is higher than any one single network model and the combination forecast model of three neural networks without preprocessing method of multiwavelet transform.     

改进GA-BPNN在短期电力负荷预测中的应用

为了避免传统方法预测短期电力负荷建模复杂性,将改进遗传算法（GA）和误差反向传播（BP）算法相结合构成的混合算法用于训练人工神经网络,结合电力负荷历史数据,对短期电力负荷进行仿真预测。仿真结果表明,该混合算法有效地解决了常规BP算法学习网络权值收敛速度慢、易陷入局部极小和GA算法独立训练神经网络速度缓慢等问题,具有较快的收敛速度和较高的预测精度。     

基于遗传神经网络的负荷预测方法

针对遗传算法早熟的缺陷,提出了改进的交叉,变异策略,采用移民算子等方法改善遗传算法的性能.并把此方法应用到神经网络的训练中,对电力系统短期负荷进行预测取得了较为理想的效果.     

基于Attention-BiLSTM-LSTM神经网络的短期电力负荷预测方法

短期电力负荷预测是电力系统中的重要问题之一,准确的预测结果可以提高电力市场的灵活性和资源利用效率,对电力系统高效运行具有重要意义.为了提高预测精度,针对电网负荷数据的时序性特征,提出一种基于Attention-BiLSTM-LSTM神经网络的短期电力负荷预测方法.该方法首先针对电力负荷的影响因素(温度、节假日等)提取特...     

蚁群神经网络在短期负荷预测的应用

为了进一步提高BP神经网络的性能,实现准确、快速预测电力系统负荷的目的,将蚁群算法(ACA)作为BP神经网络的学习算法,建立了一种新的蚁群神经网络(AcAN)预测模型.对某电力系统短期负荷预测的计算实例表明,基于蚁群神经网络的负荷预测方法与传统的BP神经网络预测方法相比,具有较强的自适应能力和较好的效果.     

A new deep intuitionistic fuzzy time series forecasting method based on long short-term memory

The Journal of Supercomputing - In recent years, deep artificial neural networks can have better forecasting performance than many other artificial neural networks. The long short-term memory...     

基于PSO-BP混合算法的短期电力负荷预测

将粒子群优化算法和BP神经网络算法相结合,形成粒子群一神经网络（PSO—BP）混合算法,建立了涉及各种影响因素的短期负荷预测模型。运用所建立的PSO-BP混合算法和BP算法的负荷预测模型进行短期负荷预测,比较所得结果可知,PSO-BP混合算法预测精度较高,效果较好。     

A methodology for short-term electric load forecasting based on specialized recursive digital filters

In this paper we propose a methodology for short-term electric load forecasting, which is adaptive and based on signal processing theory. The main interest here is to construct a next day predictor for the peak and hourly load. To this end the load data are organized into profiles according to day type and temperature interval. For each load profile, we use a specialized adaptive recursive digital filter, for which parameters are estimated on-line by using a recursive algorithm. As a result, the complete forecasting system is nonlinear and the prediction is computed based on the type and on the temperature interval of the next day. The effectiveness of the proposed methodology is illustrated by a numerical example, in which we compare performance of the proposed approach to a non-specialized and a naïve predictors, by using the Mean Absolute Percentage Error (MAPE) of the forecasting errors.     

变结构组合预测方法在短期电力负荷预测中的应用

采用变结构组合预测方法建立短期电力负荷预测模型。变结构组合预测对多种预测方法进行整合,使得在不同的预测阶段,最优的预测方法发挥的作用最大,使预测精度大大提高。     

Middle-long power load forecasting based on particle swarm optimization

Middle-long forecasting of electric power load is crucial to electric investment, which is the guarantee of the healthy development of electric industry. In this paper, the particle swarm optimization (PSO) is used as a training algorithm to obtain the weights of the single forecasting method to form the combined forecasting method. Firstly, several forecasting methods are used to do middle-long power load forecasting. Then the upper forecasting methods are measured by several indices and the entropy method is used to form a comprehensive forecasting method evaluation index, following which the PSO is used to attain a combined forecasting method (PSOCF) with the best objective function value. We then obtain the final result by adding all the results of every single forecasting method. Taking actual load data of a power grid company in North China as a sample, the results show that PSOCF model improves the forecasting precision compared to the traditional models.     

基于特征迁移学习的综合能源系统小样本日前电力负荷预测

基于历史数据和深度学习的负荷预测已广泛应用于以电能为中心的综合能源系统中以提高预测精度,然而,当区域中出现新用户时,其历史负荷数据往往极少,此时,深度学习难以适用.针对此,本文提出基于负荷特征提取和迁移学习的预测机制.首先,依据源域用户历史负荷数据,融合聚类算法和门控循环单元网络构建源域数据的特征提取和分类模型;然后,...     

A hierarchical neural model in short-term load forecasting

This paper proposes a novel neural model to the problem of short-term load forecasting (STLF). The neural model is made up of two self-organizing map (SOM) nets—one on top of the other. It has been successfully applied to domains in which the context information given by former events plays a primary role. The model was trained on load data extracted from a Brazilian electric utility, and compared to a multilayer perceptron (MLP) load forecaster. It was required to predict once every hour the electric load during the next 24 h. The paper presents the results, the conclusions, and points out some directions for future work.     

一种基于SARIMA-LSTM模型的电网主机负载预测方法

随着智能电网的不断发展,如何提高对信息设备运行状态的预测准确率以及设置适应数据变化的动态阈值区间是电网IT运维面临的巨大挑战。为了解决这些问题,提出了组合时间序列预测模型(SARIMA-LSTM),即在传统周期性ARIMA模型(SARIMA)的基础上,引入深度学习领域的LSTM模型,并摒弃了过去精度低、效果差的误差拟合方法,使用误差自回归方法来补偿预测结果。该模型可以学习到传统ARIMA模型无法捕捉到的误差波动规律,解决其无法预测非线性数据的问题。实验结果表明,在实际预测电网内存负载数据时,与ARIMA模型和SAIRIMA模型相比,SARIMA-LSTM模型可以实现更高的预测精度。     

基于多重核学习支持向量机短期负荷预测研究

近年来,支持向量机（SVM）方法在电力系统负荷预测领域的应用研究成为了热点,鉴于传统的标准支持向量机方法在预测时间和预测精度方面的不足,首次将多重核支持向量回归方法（Multiple Kernel Learning,MKL）应用于电力系统短期负荷预测领域。通过在混合核空间求解二次约束下的二次规划问题实现多重核支持向量回归算法。该方法较标准的支持向量回归算法,不仅可以提高预测性能,而且能够减少支持向量的个数。实际算例表明,该方法能够有效地提高预测精度,缩短预测时间,具有良好的泛化性能。     

数据驱动下的综合能源系统短期多元负荷预测

精准可靠的多元负荷预测对于综合能源系统规划运行具有重要的实用价值,针对园区综合能源系统多元负荷预测问题,提出一种数据驱动下的短期多元负荷预测方法。概述园区综合能源系统多能耦合的运行特点,提出适用于多元负荷相关性分析方法。基于长短时记忆网络(longshort-term memory,LSTM)、极端梯度提升(extreme gradient boosting,XGboost)模型,采用误差倒数法对LSTM、XGboost模型预测结果进行加权组合构建短期多元负荷预测模型。采用园区实际运行数据验证了组合模型的有效性,实验结果表明,相较其它两种单一预测模型,LSTM-XGboost组合模型的预测精度更高。     

加权一阶局域法在电力系统短期负荷预测中的应用

在混沌时间序列的基础上提出加权一阶局域法, 并将其应用于电力系统历史负荷数据序列进行预测. 通过对华东某电网实际负荷数据进行预测, 结果令人满意.