Long-Term Electrical Load Forecasting in Rwanda Based on Support Vector Machine Enhanced with Q-SVM Optimization Kernel Function

In recent years, Rwanda’s rapid economic development has created the “Rwanda Africa Wonder”, but it has also led to a substantial increase in energy consumption with the ambitious goal of reaching universal access by 2024. Meanwhile, on the basis of the rapid and dynamic connection of new households, there is uncertainty about generating, importing, and exporting energy whichever imposes a significant barrier. Long-Term Load Forecasting (LTLF) will be a key to the country’s utility plan to examine the dynamic electrical load demand growth patterns and facilitate long-term planning for better and more accurate power system master plan expansion. However, a Support Vector Machine (SVM) for long-term electric load forecasting is presented in this paper for accurate load mix planning. Considering that an individual forecasting model usually cannot work properly for LTLF, a hybrid Q-SVM will be introduced to improve forecasting accuracy. Finally, effectively assess model performance and efficiency, error metrics, and model benchmark parameters there assessed. The case study demonstrates that the new strategy is quite useful to improve LTLF accuracy. The historical electric load data of Rwanda Energy Group (REG), a national utility company from 1998 to 2020 was used to test the forecast model. The simulation results demonstrate the proposed algorithm enhanced better forecasting accuracy.

Medium-Term Electric Load Forecasting Using Multivariable Linear and Non-Linear Regression

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.

Long Term Load Forecasting and Recommendations for China Based on Support Vector Regression

Long-term load forecasting (LTLF) is a challenging task because of the complex relationships between load and factors affecting load. However, it is crucial for the economic growth of fast developing countries like China as the growth rate of gross domestic product (GDP) is expected to be 7.5%, according to China’s 11th Five-Year Plan (2006-2010). In this paper, LTLF with an economic factor, GDP, is implemented. A support vector regression (SVR) is applied as the training algorithm to obtain the nonlinear relationship between load and the economic factor GDP to improve the accuracy of forecasting.

Long-Term Load Forecasting of Southern Governorates of Jordan Distribution Electric System

Load forecasting is vitally important for electric industry in the deregulated economy. This paper aims to face the power crisis and to achieve energy security in Jordan. Our participation is localized in the southern parts of Jordan including, Ma’an, Karak and Aqaba. The available statistical data about the load of southern part of Jordan are supplied by electricity Distribution Company. Mathematical and statistical methods attempted to forecast future demand by determining trends of past results and use the trends to extrapolate the curve demand in the future.

Medium Term Load Forecasting for Jordan Electric Power System Using Particle Swarm Optimization Algorithm Based on Least Square Regression Methods

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.

Optimal Scheme with Load Forecasting for Demand Side Management (DSM) in Residential Areas

Utilities around the world have been considering Demand Side Management (DSM) in their strategic planning. The costs of constructing and operating a new capacity generation unit are increasing everyday as well as Transmission and distribution and land issues for new generation plants, which force the utilities to search for another alternatives without any additional constraints on customers comfort level or quality of delivered product. De can be defined as the selection, planning, and implementation of measures intended to have an influence on the demand or customer-side of the electric meter, either caused directly or stimulated indirectly by the utility. DSM programs are peak clipping, Valley filling, Load shifting, Load building, energy conservation and flexible load shape. The main Target of this paper is to show the relation between DSM and Load Forecasting. Moreover, it highlights on the effect of applying DSM on Forecasted demands and how this affects the planning strategies for utility companies. This target will be clearly illustrated through applying the developed algorithm in this paper on an existing residential compound in Cairo-Egypt.

Deep Learning Network for Energy Storage Scheduling in Power Market Environment Short-Term Load Forecasting Model

In the electricity market,fluctuations in real-time prices are unstable,and changes in short-term load are determined by many factors.By studying the timing of charging and discharging,as well as the economic benefits of energy storage in the process of participating in the power market,this paper takes energy storage scheduling as merely one factor affecting short-term power load,which affects short-term load time series along with time-of-use price,holidays,and temperature.A deep learning network is used to predict the short-term load,a convolutional neural network(CNN)is used to extract the features,and a long short-term memory(LSTM)network is used to learn the temporal characteristics of the load value,which can effectively improve prediction accuracy.Taking the load data of a certain region as an example,the CNN-LSTM prediction model is compared with the single LSTM prediction model.The experimental results show that the CNN-LSTM deep learning network with the participation of energy storage in dispatching can have high prediction accuracy for short-term power load forecasting.

基于改进Q学习算法和组合模型的超短期电力负荷预测

单一模型在进行超短期负荷预测时会因负荷波动而导致预测精度变差,针对此问题,提出一种基于深度学习算法的组合预测模型｡首先,采用变分模态分解对原始负荷序列进行分解,得到一系列的子序列。其次,分别采用双向长短期记忆网络和优化后的深度极限学习机对每个子序列进行预测。然后,利用改进Q学习算法对双向长短期记忆网络的预测结果和深度极限学习机的预测结果进行加权组合,得到每个子序列的预测结果。最后,将各个子序列的预测结果进行求和,得到最终的负荷预测结果。以某地真实负荷数据进行预测实验,结果表明所提预测模型较其他模型在超短期负荷预测中表现更佳,预测精度达到98%以上。

基于多模型融合的中长期径流集成预测方法

中长期水文预报是流域水资源规划与合理配置的重要依据。为提高中长期径流预测精度,提出了一种基于多模型融合的水库中长期径流集成预测方法。该方法将ARMA、BP、LSTM、RF和SVR等5个异质预测模型进行融合,同时采用超参数优化方法确定各模型的最优参数。将其用于青海省龙羊峡水库的中长期径流预报中,结果表明,通过Stacking融合算法建立的集成预测模型相较于单一模型,取得了更高的预测精度(R2值由0.71提升至0.82)。此方法可为提升流域中长期径流预测精度提供一定参考。

基于Stacking融合的LSTM-SA-RBF短期负荷预测

为了解决单个神经网络预测的局限性和时间序列的波动性,提出了一种奇异谱分析(singular spectrum analysis,SSA)和Stacking框架相结合的短期负荷预测方法。利用随机森林筛选出与历史负荷相关性强烈的特征因素,采用SSA为负荷数据降噪,简化模型计算过程;基于Stacking框架,结合长短期记忆(long and short-term memory,LSTM)-自注意力机制(self-attention mechanism,SA)、径向基(radial base functions,RBF)神经网络和线性回归方法集成新的组合模型,同时利用交叉验证方法避免模型过拟合;选取PJM和澳大利亚电力负荷数据集进行验证。仿真结果表明,与其他模型比较,所提模型预测精度高。

基于集群辨识和卷积神经网络-双向长短期记忆-时序模式注意力机制的区域级短期负荷预测

为了解决区域级短期电力负荷预测时输入特征过多和负荷时序性较强的问题,提出一种基于集群辨识和卷积神经网络(convolutional neural networks,CNN)-双向长短期记忆网络(bi-directional long short-term memory,BiLSTM)-时序模式注意力机制(temporal pattern attention,TPA)的预测方法。首先,将用电模式和天气作为影响因素,基于二阶聚类算法对区域内的负荷节点进行集群辨识,再从每个集群中挑选代表特征作为深度学习模型的输入,这样既能减少输入特征维度,降低计算复杂度,又能综合考虑预测区域的整体特征,提升预测精度。然后,针对区域电力负荷时序性的特点,用CNN-BiLSTM-TPA模型完成训练和预测,该模型能提取输入数据的双向信息生成隐状态矩阵,并对隐状态矩阵的重要特征加权,从多时间步上捕获双向时序信息用于预测。最后,在美国加利福尼亚州实例上分析验证了所提方法的有效性。

基于双模态分解的发电站母线短期负荷预测

母线负荷预测是电力系统运营和规划中至关重要的一项任务,针对电力负荷数据的非线性强以及影响因素多等问题,提出了一种基于双模态分解、深度学习和注意力机制的负荷预测模型。首先,对输入数据进行经验模态分解(Empirical mode decomposition,EMD),通过K-means聚类分析对复杂度相似的分量进行集合得到三个组合分量。其次,使用变分模态分解(Variational mode decomposition, VMD)对组合分量再次进行分解得到不同分量,使用麻雀搜索算法(Sparrow search algorithm,SSA)对变分模态分解的参数进行优化。再次,将变分模态分解得到的分量与影响因素连接并输入长短期记忆网络(Long short-term memory network, LSTM),通过注意力机制挖掘数据内部的相关性,并使用SSA对LSTM网络的参数进行优化。最后,采用宁夏某电站一年的负荷数据进行验证,经过与不同模型的对比分析,所提模型有更高的预测精度。

基于变分模态分解的卷积长短时记忆网络短期电力负荷预测方法

电力负荷序列易受多重外部因素影响而呈现复杂性,不利于精准预测。为此,提出一种基于变分模态分解(variational mode decomposition,VMD)的卷积神经网络和长短期记忆网络(convolutional neural network and long short-term memory network,CNN-LSTM)相结合的短期电力负荷并行预测方法。先采用VMD将负荷数据分解为规律性强的各本征模态函数(intrinsic mode function,IMF)及残差;再将各分量分别输入到各自对应的CNN-LSTM混合预测网络,获得各初始预测值,并将该值与由气候、日期类型等组合得到的相关因素特征集相结合,进一步得出修正预测值;最终,叠加各分量修正预测值即得到完整预测结果。在实际负荷数据上做验证分析,结果表明,考虑相关外部因素特征集后日负荷预测平均相对误差均值可降低2.18%。与几种常规负荷预测方法进行效果对比,验证了该方法的有效性和可行性。

基于超参数优化的电力负荷预测模型研究

电力负荷数据的多样性与复杂性,会导致负荷预测过程中出现超参数难以确定、拟合效果较差和预测精度不高等问题。针对以上问题,提出一种基于樽海鞘群算法的融入注意力机制的双向长短期记忆神经网络模型——SSA-AM-BiLSTM模型。该模型使用BiLSTM学习特征的内部变化规律,引入注意力机制为特征进行权重分配,并且利用樽海鞘群算法优化网络超参数。基于具体数据集进行的负荷预测仿真实验表明,相较于GRU、LSTM、AM-BiLSTM和PSO-AM-BiLSTM模型,所提出的SSA-AM-BiLSTM模型的MAPE分别减少了2.15%、1.93%、1.42%和0.45%,并且优化了拟合效果,显著提高了预测精度。

基于集合Kalman滤波的中长期径流预报

为降低中长期径流预报的不确定性,增加水电站水库的发电效益,针对现有方法侧重于提高单一预报模型确定性预报结果的准确性以降低径流预报不确定性的问题,提出一种基于集合Kalman滤波的入库径流确定性预报方法。以旬为预见期的锦西水库实例验证结果表明:相比传统的单一预报模型和传统的信息融合预报模型,基于集合Kalman滤波的中长期径流预报可使RMSE降低4.78 m^(3)/s,合格率可提高0.56%,且更有效地降低了汛期预报的不确定性,得到了更加准确、可靠的确定性径流预报结果,可为开展流域梯级水电站优化调度提供技术支持。

基于变分模态分解和复合变量选取的短期负荷预测

精准的短期负荷预测是实现电网精益化运行和管理重要保障,但存在短期负荷波动性强、负荷预测关键影响因素选取困难等精准预测难题。利用变分模态分解将原始电力负荷数据分解为多个子序列,挖掘短期负荷波动特征的同时避免模态混叠问题,提出复合变量选取算法分析筛选影响负荷波动的关键因素,有效去除预测干扰信息并进一步简化预测模型的复杂度,通过兼顾数据短期依赖和长期依赖的长短时记忆神经网络对各子序列进行预测,并将各子序列预测结果进行叠加实现最终的短期负荷预测,据此建立基于变分模态分解和复合变量选取的短期负荷预测方法。选取2019年整年长沙市实际数据验证结果表明,提出算法在复杂外部影响因素下,能准确筛选负荷预测的关键影响因素,相比传统预测模型,提出模型结构更简单、预测精度更高。

基于典型源荷耦合模式的中长期时序场景生成

高比例可再生能源电力系统中源荷功率在气象的影响下耦合程度增加。基于生成式模型的场景生成方法是分析源荷功率不确定性问题的成熟工具,然而实际应用中常受源荷数据匹配样本少的问题限制,直接生成的中长期源荷耦合时序场景质量较差。提出一种基于典型源荷耦合模式的中长期时序场景生成方法。首先,定义了源荷各自的气象模式概念。然后,通过气象距离将历史数据中典型气象模式相似的源荷聚合成定义的典型源荷耦合模式。接着,采用生成对抗网络生成典型风光场景,并将生成场景与典型源荷耦合模式进行匹配。最后,根据匹配的对应关系得到典型源荷耦合场景。以日本地区为例的仿真结果表明,所提方法能够提高场景在气象方面的可解释性,同时减小了源荷数据匹配样本少对中长期源荷耦合场景生成的影响。

带月池船舶的波浪载荷计算研究

由于“奋力号”打捞船的用途特殊、自带开底月池且L/B>5及B/D≤2.5,已不满足采用规范公式计算波浪载荷的条件,故需进一步用直接计算的方法确定其波浪载荷。以“奋力号”为例,简要介绍利用三维频域线性水动力计算软件SESAM,计算“奋力号”在调遣和工作工况时,波浪弯矩和波浪剪力的长期及短期预报值,并与规范公式计算的波浪弯矩剪力值进行对比,为后续“奋力号”全船结构强度有限元分析中波浪载荷的施加提供依据。

基于EEMD-AE-LSTM的生活用电短期负荷预测

生活用电负荷随机性高,使用单一的预测模型进行预测会造成预测结果精度不高并且预测时间比较长。建立集合经验模态分解(EEMD)-自动编码器(AE)-长短期记忆网络(LSTM)的组合预测模型用来预测生活用电短期负荷。采用EEMD算法将负荷数据分解为有限个本征模态分量(IMF)和一个残差分量,与自动编码器训练得到的特征序列组合,并建立LSTM模型预测线性加权产生最终预测结果。实验结果表明,相对于其他模型,EEMD-AE-LSTM模型的预测精度更高,是一种较为有效的生活用电短期负荷预测方法。