Short-Term Household Load Forecasting Based on Attention Mechanism and CNN-ICPSO-LSTM

Accurate load forecasting forms a crucial foundation for implementing household demand response plans andoptimizing load scheduling. When dealing with short-term load data characterized by substantial fluctuations,a single prediction model is hard to capture temporal features effectively, resulting in diminished predictionaccuracy. In this study, a hybrid deep learning framework that integrates attention mechanism, convolution neuralnetwork (CNN), improved chaotic particle swarm optimization (ICPSO), and long short-term memory (LSTM), isproposed for short-term household load forecasting. Firstly, the CNN model is employed to extract features fromthe original data, enhancing the quality of data features. Subsequently, the moving average method is used for datapreprocessing, followed by the application of the LSTM network to predict the processed data. Moreover, the ICPSOalgorithm is introduced to optimize the parameters of LSTM, aimed at boosting the model’s running speed andaccuracy. Finally, the attention mechanism is employed to optimize the output value of LSTM, effectively addressinginformation loss in LSTM induced by lengthy sequences and further elevating prediction accuracy. According tothe numerical analysis, the accuracy and effectiveness of the proposed hybrid model have been verified. It canexplore data features adeptly, achieving superior prediction accuracy compared to other forecasting methods forthe household load exhibiting significant fluctuations across different seasons.

A Levenberg–Marquardt Based Neural Network for Short-Term Load Forecasting

Short-term load forecasting (STLF) is part and parcel of theefficient working of power grid stations. Accurate forecasts help to detect thefault and enhance grid reliability for organizing sufficient energy transactions.STLF ranges from an hour ahead prediction to a day ahead prediction. Variouselectric load forecasting methods have been used in literature for electricitygeneration planning to meet future load demand. A perfect balance regardinggeneration and utilization is still lacking to avoid extra generation and misusageof electric load. Therefore, this paper utilizes Levenberg–Marquardt(LM) based Artificial Neural Network (ANN) technique to forecast theshort-term electricity load for smart grids in a much better, more precise,and more accurate manner. For proper load forecasting, we take the mostcritical weather parameters along with historical load data in the form of timeseries grouped into seasons, i.e., winter and summer. Further, the presentedmodel deals with each season’s load data by splitting it into weekdays andweekends. The historical load data of three years have been used to forecastweek-ahead and day-ahead load demand after every thirty minutes makingload forecast for a very short period. The proposed model is optimized usingthe Levenberg-Marquardt backpropagation algorithm to achieve results withcomparable statistics. Mean Absolute Percent Error (MAPE), Root MeanSquared Error (RMSE), R2, and R are used to evaluate the model. Comparedwith other recent machine learning-based mechanisms, our model presentsthe best experimental results with MAPE and R2 scores of 1.3 and 0.99,respectively. The results prove that the proposed LM-based ANN modelperforms much better in accuracy and has the lowest error rates as comparedto existing work.

Research on Short-Term Load Forecasting of Distribution Stations Based on the Clustering Improvement Fuzzy Time Series Algorithm

An improved fuzzy time series algorithmbased on clustering is designed in this paper.The algorithm is successfully applied to short-term load forecasting in the distribution stations.Firstly,the K-means clustering method is used to cluster the data,and the midpoint of two adjacent clustering centers is taken as the dividing point of domain division.On this basis,the data is fuzzed to form a fuzzy time series.Secondly,a high-order fuzzy relation with multiple antecedents is established according to the main measurement indexes of power load,which is used to predict the short-term trend change of load in the distribution stations.Matlab/Simulink simulation results show that the load forecasting errors of the typical fuzzy time series on the time scale of one day and one week are[−50,20]and[−50,30],while the load forecasting errors of the improved fuzzy time series on the time scale of one day and one week are[−20,15]and[−20,25].It shows that the fuzzy time series algorithm improved by clustering improves the prediction accuracy and can effectively predict the short-term load trend of distribution stations.

Short-Term Power Load Forecasting with Hybrid TPA-BiLSTM Prediction Model Based on CSSA

Since the existing prediction methods have encountered difficulties in processing themultiple influencing factors in short-term power load forecasting,we propose a bidirectional long short-term memory(BiLSTM)neural network model based on the temporal pattern attention(TPA)mechanism.Firstly,based on the grey relational analysis,datasets similar to forecast day are obtained.Secondly,thebidirectional LSTM layermodels the data of thehistorical load,temperature,humidity,and date-type and extracts complex relationships between data from the hidden row vectors obtained by the BiLSTM network,so that the influencing factors(with different characteristics)can select relevant information from different time steps to reduce the prediction error of the model.Simultaneously,the complex and nonlinear dependencies between time steps and sequences are extracted by the TPA mechanism,so the attention weight vector is constructed for the hidden layer output of BiLSTM and the relevant variables at different time steps are weighted to influence the input.Finally,the chaotic sparrow search algorithm(CSSA)is used to optimize the hyperparameter selection of the model.The short-term power load forecasting on different data sets shows that the average absolute errors of short-termpower load forecasting based on our method are 0.876 and 4.238,respectively,which is lower than other forecastingmethods,demonstrating the accuracy and stability of our model.

Intelligent Load Management Scheme for a Residential Community in Smart Grids Network Using Fair Emergency Demand Response Programs

In the framework of liberalized deregulated electricity market, dynamic competitive environment exists between wholesale and retail dealers for energy supplying and management. Smart Grids topology in form of energy management has forced power supplying agencies to become globally competitive. Demand Response (DR) Programs in context with smart energy network have influenced prosumers and consumers towards it. In this paper Fair Emergency Demand Response Program (FEDRP) is integrated for managing the loads intelligently by using the platform of Smart Grids for Residential Setup. The paper also provides detailed modelling and analysis of respective demands of residential consumers in relation with economic load model for FEDRP. Due to increased customer’s partaking in this program the load on the utility is reduced and managed intelligently during emergency hours by providing fair and attractive incentives to residential clients, thus shifting peak load to off peak hours. The numerical and graphical results are matched for intelligent load management scenario.

Theory Study and Application of the BP-ANN Method for Power Grid Short-Term Load Forecasting

Aiming at the low accuracy problem of power system short-term load forecasting by traditional methods, a back-propagation artificial neural network （BP-ANN） based method for short-term load forecasting is presented in this paper. The forecast points are related to prophase adjacent data as well as the periodical long-term historical load data. Then the short-term load forecasting model of Shanxi Power Grid （China） based on BP-ANN method and correlation analysis is established. The simulation model matches well with practical power system load, indicating the BP-ANN method is simple and with higher precision and practicality.

Forecasting of Short-term Load based on LMD and BBO-RBF Model

Short-term load forecasting is a basis of power system dispatching and operation. In order to improve the short term power load precision, a novel approach for short-term load forecasting is presented based on local mean decomposition (LMD) and the radial basis function neural network method (RBFNN). Firstly, the decomposition of LMD method based on characteristics of load data then the decomposed data are respectively predicted by using the RBF network model and predicted by using the BBO-RBF network model. The simulation results show that the RBF network model optimized by using BBO algorithm is optimized in error performance index, and the prediction accuracy is higher and more effective.

Short-term load forecasting based on fuzzy neural network

The fuzzy neural network is applied to the short-term load forecasting. The fuzzy rules and fuzzy membership functions of the network are obtained through fuzzy neural network learming. Three inference algorithms, i.e. themultiplicative inference, the maximum inference and the minimum inference, are used for comparison. The learningalgorithms corresponding to the inference methods are derived from back-propagation algorithm. To validate the fuzzyneural network model, the network is used to Predict short-term load by compaing the network output against the realload data from a local power system supplying electricity to a large steel manufacturer. The experimental results aresatisfactory.

Short-Term Load Forecasting Using Radial Basis Function Neural Network

An accurate short-term forecasting method for load of electric power system can help the electric power system’s operator to reduce the risk of unreliability of electricity supply. This paper proposed a radial basis function (RBF) neural network method to forecast the short-term load of electric power system. To demonstrate the effectiveness of the proposed method, the method is tested on the practical load data information of the Tai power system. The good agreements between the realistic values and forecasting values are obtained;the numerical results show that the proposed forecasting method is accurate and reliable.

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.

Load-forecasting method for IES based on LSTM and dynamic similar days with multi-features

To fully exploit the rich characteristic variation laws of an integrated energy system(IES)and further improve the short-term load-forecasting accuracy,a load-forecasting method is proposed for an IES based on LSTM and dynamic similar days with multi-features.Feature expansion was performed to construct a comprehensive load day covering the load and meteorological information with coarse and fine time granularity,far and near time periods.The Gaussian mixture model(GMM)was used to divide the scene of the comprehensive load day,and gray correlation analysis was used to match the scene with the coarse time granularity characteristics of the day to be forecasted.Five typical days with the highest correlation with the day to be predicted in the scene were selected to construct a“dynamic similar day”by weighting.The key features of adjacent days and dynamic similar days were used to forecast multi-loads with fine time granularity using LSTM.Comparing the static features as input and the selection method of similar days based on non-extended single features,the effectiveness of the proposed prediction method was verified.

计及可中断负荷参与应急调度的备用容量优化配置方法

新能源大规模并网增加了电网频率失稳的风险。针对大功率缺失下的频率安全问题,提出1种计及可中断负荷参与应急调度的备用容量优化配置方法。相较于传统的备用容量优化方案,所提方法考虑应急调度过程中常规机组和可中断负荷的响应时间和爬坡速率,构建考虑分段频率下限值约束的应急备用容量优化模型,以提高电网的应急处理能力。算例分析表明,所提方法能够兼顾电力系统经济调度和频率稳定目标,提高电网安全经济运行水平。

电力系统两阶段紧急切负荷控制智能预决策

电力系统仿真分析是安全稳定控制领域重要技术,可以用于制定与校验紧急控制措施。传统的人工分析仿真数据以决策紧急控制措施的工作模式严重依赖专家经验,在应用于复杂大电网时显得耗时耗力。该文提出一种两阶段紧急切负荷控制智能预决策方法,第一阶段决策切负荷点,第二阶段决策切负荷量。首先基于仿真数据,区分3种电压失稳模式:纯电压失稳、耦合电压失稳和混合电压失稳,分别采用不同的负荷筛选方法;然后基于轻量级梯度提升机算法,根据仿真数据直接预估系统恢复稳定所需的切负荷总量,按负荷排序进行分配。结合暂稳仿真校验控制措施的有效性,调整决策量。以我国东北电网为例进行仿真研究,验证了在大电网紧急控制措施制定时,所提两阶段智能决策方法相比完全迭代试凑方法在有效性、快速性和准确性方面的优势。

急诊护士认知负荷现状及影响因素的研究

目的探究急诊科护士的认知负荷水平和相关影响因素。方法采用便利抽样法,选取2022年4月至5月中日友好医院的48名急诊科护士为研究对象,收集其一般资料并测量总认知负荷和3种不同类型的认知负荷。结果回归分析显示,护患关系是急诊科护士总认知负荷的影响因素(β=6.202,P=0.007),工作环境嘈杂情况是内在认知负荷的影响因素(β=1.042,P<0.001),居住情况(与他人合租)是外在认知负荷的影响因素(β=-3.917,P=0.006),尚未发现关联认知负荷的影响因素。结论急诊科护士总认知负荷及3种类型认知负荷水平均较高,护理管理者可通过调控相关影响因素降低急诊科护士认知负荷水平,进而提升护理工作效率。

Convolution Neural Network-based Load Model Parameter Selection Considering Short-term Voltage Stability

The recently proposed ambient signal-based load modeling approach offers an important and effective idea to study the time-varying and distributed characteristics of power loads.Meanwhile,it also brings new problems.Since the load model parameters of power loads can be obtained in real-time for each load bus,the numerous identified parameters make parameter application difficult.In order to obtain the parameters suitable for off-line applications,load model parameter selection(LMPS)is first introduced in this paper.Meanwhile,the convolution neural network(CNN)is adopted to achieve the selection purpose from the perspective of short-term voltage stability.To begin with,the field phasor measurement unit(PMU)data from China Southern Power Grid are obtained for load model parameter identification,and the identification results of different substations during different times indicate the necessity of LMPS.Meanwhile,the simulation case of Guangdong Power Grid shows the process of LMPS,and the results from the CNNbased LMPS confirm its effectiveness.

民用飞机主起落架断离销结构设计及断离失效分析

起落架在承受过载工况时应能够实现与机体安全快速分离,为了明确民用飞机主起落架应急断离销断离槽结构对其断离载荷的影响,分别设计外槽、长外槽、内槽和长内槽4种断离槽结构,仿真分析断离结构对断离销断离载荷的影响;试验对比分析内、外槽结构的断离销断离载荷和失效模式。结果表明:断离销破坏失效从断离槽内部扩展至外部,断离载荷随受剪截面积的增加而增加,且呈现线性变化趋势;通孔销断离载荷随受剪截面积的变化量(剪切强度系数)最大,外断离槽销次之,内断离槽销最小;内断离槽销受剪切截面积和整体尺寸的影响最小,断离载荷容易控制;断口形貌显示外断离槽销为韧性断裂失效,内断离槽销则表现出脆性断裂失效,内断离槽销的设计符合民用飞机主起落架应急断离适航设计要求。

炼化企业氢气平衡与优化

炼化企业氢气平衡与优化是一个涉及工艺优化、能源管理、环保等方面的综合问题,旨在提高氢气的利用效率,增加效益。为了提高氢气系统管理水平,确保炼化企业临氢装置运行平稳,以某炼油厂为研究对象,对全厂氢源、氢阱现状进行分析,根据氢源压力不同,分2.0MPa和3.0MPa两级向氢阱供应氢气;对正常生产工况和应急工况的氢气平衡控制方式进行总结,提出了氢气系统压力过剩、不足或氢气中断应急处置原则和恢复方案;按氢气纯度对装置的影响,增加了高纯度氢气供硫黄回收装置、某企业2条流程,确保两套装置稳定运行;分析1号加氢装置柴油密度与氢气纯度关系,讨论优化措施;同时根据产氢成本高低,对降低制氢装置负荷、提高连续重整装置负荷、多产廉价氢气进行探讨;根据制氢装置负荷情况,优化氢气压缩机运行模式,6个月增加效益约50.7万元。

备用电源与应急电源对比及供电方案探讨

对比多本国家标准及规范对备用电源和应急电源术语的定义;分析二者的区别和侧重点;提出供配电系统设计时应充分理解备用电源和应急电源的定义,根据工程的负荷性质、用电容量、工程特点、系统规模和发展规划及当地供电条件进行多方案比选,并对5种备用电源供电方案和3种应急电源供电方案的特点和适用范围进行探讨。

紧急弹射时G值载荷及头盔对颈部损伤的影响

文中研究了飞行员在紧急弹射时不同G值载荷和头盔特性对颈部损伤的潜在风险。结合刚体和有限元模型建立了正常人体的头颈模型,并通过设置飞行员弹射时的边界条件以及不同加速度载荷,模拟飞行员弹射时佩戴不同头盔的运动历程。结果显示:高G载荷导致颈部稳定性降低且椎间盘应力随之增大,颈部损伤标准(neck injury criterion,NIC)损伤随载荷的增大而增大。头盔质量的增加导致颈部载荷增大,椎间盘C4-C5节段,佩戴轻型头盔比无头盔时椎间盘应力增大30.84%,佩戴重型头盔比佩戴轻型头盔时增大40.91%,所以重型头盔对颈部损伤的风险高于轻型头盔。韧带所受的拉力与头盔质量的增大成正比,并且头盔重心后移可能增大韧带拉伤的风险。

伤员升降辅助装置的设计

目的:设计一种伤员升降辅助装置,以提高应急救援任务中伤员的装卸效率。方法:该装置由升降结构、电动控制单元及手动控制单元组成。整个装置采用滑轮组来实现省力,利用可分离、可移动的睡垫来实现横向平移,通过配置电动机和手动摇杆套件实现电动和手动2种控制模式。为验证该装置的性能,制作等比例缩小模型测试摇杆端在0、5、10、20、30 kg承重下的受力。结果:在5组不同载重下,摇杆端所受的拉力测试值趋势符合理论分析结果。结论:该装置操作方便,在节省人力的同时能够更好地保证伤员安全,提高了伤员装卸效率。