Dynamic GM（1,1） Model Based on Cubic Spline for Electricity Consumption Prediction in Smart Grid

Electricity demand forecasting plays an important role in smart grid expansion planning.In this paper,we present a dynamic GM(1,1) model based on grey system theory and cubic spline function interpolation principle.Using piecewise polynomial interpolation thought,this model can dynamically predict the general trend of time series data.Combined with low-order polynomial,the cubic spline interpolation has smaller error,avoids the Runge phenomenon of high-order polynomial,and has better approximation effect.Meanwhile,prediction is implemented with the newest information according to the rolling and feedback mechanism and fluctuating error is controlled well to improve prediction accuracy in time-varying environment.Case study using the living electricity consumption data of Jiangsu province in 2008 is presented to demonstrate the effectiveness of the proposed model.

Analysis and Prediction of Regional Electricity Consumption Based on BP Neural Network

Electricity consumption forecasting is one of the most important tasks for power system workers,and plays an important role in regional power systems.Due to the difference in the trend of power load and the past in the new normal,the influencing factors are more diversified,which makes it more difficult to predict the current electricity consumption.In this paper,the grey system theory and BP neural network are combined to predict the annual electricity consumption in Jiangsu.According to the historical data of annual electricity consumption and the six factors affecting electricity consumption,the gray correlation analysis method is used to screen the important factors,and three factors with large correlation degree are selected as the input parameters of BP neural network.The power forecasting model uses nearly 18 years of data to train and validate the model.The results show that the gray correlation analysis and BP neural network method have higher accuracy in power consumption prediction,and the calculation is more convenient than traditional methods.

Machine Learning Empowered Electricity Consumption Prediction

Electricity,being the most efficient secondary energy,contributes for a larger proportion of overall energy usage.Due to a lack of storage for energy resources,over supply will result in energy dissipation and substantial investment waste.Accurate electricity consumption prediction is vital because it allows for the preparation of potential power generation systems to satisfy the growing demands for electrical energy as well as:smart distributed grids,assessing the degree of socioeconomic growth,distributed system design,tariff plans,demand-side management,power generation planning,and providing electricity supply stability by balancing the amount of electricity produced and consumed.This paper proposes amedium-termprediction model that can predict electricity consumption for a given location in Saudi Arabia.Hence,this study implemented a standalone ArtificialNeuralNetwork(ANN)model and bagging ensemble for predicting total monthly electricity consumption in 18 locations across Saudi Arabia.The dataset used in this research is gathered exclusively from the Saudi Electric Company.The pre-processing phase included normalizing the data using min-max method and mapping the cyclical attribute to its sine and cosine facets.The number of neurons and learning rate of the standalone model were optimized using hyperparameter tuning.Finally,the standalone model was tested against the bagging ensemble using the optimized ANN.The bagging ensemble with an optimized ANN as the chosen classifier outperformed the standalone ANN model.The results for the proposed model produced 0.9116 Correlation Coefficient(CC),0.2836 Mean Absolute Percentage Error(MAPE),0.4578,Root Mean Squared Percentage Error(RMSPE),0.0298 MAE,and 0.069 Root Mean Squared Error(RMSE),respectively.

Monthly Electricity Consumption Forecast Based on Multi-Target Regression

Urban grid power forecasting is one of the important tasks of power system operators, which helps to analyze the development trend of the city. As the demand for electricity in various industries is affected by many factors, the data of relevant influencing factors are scarce, resulting in great deviations in the accuracy of prediction results. In order to improve the prediction results, this paper proposes a model based on Multi-Target Tree Regression to predict the monthly electricity consumption of different industrial structures. Due to few data characteristics of actual electricity consumption in Shanghai from 2013 to the first half of 2017. Thus, we collect data on GDP growth, weather conditions, and tourism season distribution in various industries in Shanghai, model and train the electricity consumption data of different industries in different months. The multi-target tree regression model was tested with actual values to verify the reliability of the model and predict the monthly electricity consumption of each industry in the second half of 2017. The experimental results show that the model can accurately predict the monthly electricity consumption of various industries.

Characteristics of electricity consumption of different industry types considering atmospheric condition

Reducing greenhouse gases (RHG) is going on actively in the international movement. In the field of architecture, RHG is an inevitable work. To establish a plan for RHG, firstly we need to reduce energy consumption. Greenhouse gas generated by energy consumption is the main cause of global warming. For this we should know that how much electricity consumption we use. The research targets of this study are commercial buildings with various businesses. Their electricity consumption was analyzed by business units rather than buildings. Each business was divided into 13 sectors according to industrial classification and electricity consumption was analyzed for each industry. For commercial buildings, the electricity consumption is done by the private sector and construction management is an autonomy system in private instead of an integrated management system. In this study, we classified and analyzed the electricity consumption characteristics according to collected data, analyzed the relationship between the electricity consumption with atmospheric temperature through SPSS, and developed an electricity prediction model.

A Novel Rolling and Fractional-ordered Grey System Model and Its Application for Predicting Industrial Electricity Consumption

Accurate and reasonable prediction of industrial electricity consumption is of great significance for promoting regional green transformation and optimizing the energy structure.However,the regional power system is complicated and uncertain,affected by multiple factors including climate,population and economy.This paper incorporates structure expansion,parameter optimization and rolling mechanism into a system forecasting framework,and designs a novel rolling and fractional-ordered grey system model to forecast the industrial electricity consumption,improving the accuracy of the traditional grey models.The optimal fractional order is obtained by using the particle swarm optimization algorithm,which enhances the model adaptability.Then,the proposed model is employed to forecast and analyze the changing trend of industrial electricity consumption in Fujian province.Experimental results show that industrial electricity consumption in Fujian will maintain an upward growth and it is expected to 186.312 billion kWh in 2026.Compared with other seven benchmark prediction models,the proposed grey system model performs best in terms of both simulation and prediction performance metrics,providing scientific reference for regional energy planning and electricity market operation.

Forecasting Energy Consumption Using a Novel Hybrid Dipper Throated Optimization and Stochastic Fractal Search Algorithm

The accurate prediction of energy consumption has effective role in decision making and risk management for individuals and governments.Meanwhile,the accurate prediction can be realized using the recent advances in machine learning and predictive models.This research proposes a novel approach for energy consumption forecasting based on a new optimization algorithm and a new forecasting model consisting of a set of long short-term memory(LSTM)units.The proposed optimization algorithm is used to optimize the parameters of the LSTM-based model to boost its forecasting accuracy.This optimization algorithm is based on the recently emerged dipper-throated optimization(DTO)and stochastic fractal search(SFS)algo-rithm and is referred to as dynamic DTOSFS.To prove the effectiveness and superiority of the proposed approach,five standard benchmark algorithms,namely,stochastic fractal search(SFS),dipper throated optimization(DTO),whale optimization algorithm(WOA),particle swarm optimization(PSO),and grey wolf optimization(GWO),are used to optimize the parameters of the LSTM-based model,and the results are compared with that of the proposed approach.Experimental results show that the proposed DDTOSFS+LSTM can accurately forecast the energy consumption with root mean square error RMSE of 0.00013,which is the best among the recorded results of the other methods.In addition,statistical experiments are conducted to prove the statistical difference of the proposed model.The results of these tests confirmed the expected outcomes.

Combined Prediction for Vehicle Speed with Fixed Route

Achieving accurate speed prediction provides the most critical support parameter for high-level energy management of plug-in hybrid electric vehicles.Nowadays,people often drive a vehicle on fixed routes in their daily travels and accurate speed predictions of these routes are possible with random prediction and machine learning,but the prediction accuracy still needs to be improved.The prediction accuracy of traditional prediction algorithms is difficult to further improve after reaching a certain accuracy;problems,such as over fitting,occur in the process of improving prediction accuracy.The combined prediction model proposed in this paper can abandon the transitional dependence on a single prediction.By combining the two prediction algorithms,the fusion of prediction performance is achieved,the limit of the single prediction performance is crossed,and the goal of improving vehicle speed prediction performance is achieved.In this paper,an extraction method suitable for fixed route vehicle speed is designed.The application of Markov and back propagation(BP)neural network in predictions is introduced.Three new combined prediction methods,all named Markov and BP Neural Network(MBNN)combined prediction algorithm,are proposed,which make full use of the advantages of Markov and BP neural network algorithms.Finally,the comparison among the prediction methods has been carried out.The results show that the three MBNN models have improved by about 19%,28%,and 29%compared with the Markov prediction model,which has better performance in the single prediction models.Overall,the MBNN combined prediction models can improve the prediction accuracy by 25.3%on average,which provides important support for the possible optimization of plug-in hybrid electric vehicle energy consumption.

Data-driven sensitivity analysis and electricity consumption prediction for water source heat pump system using limited information

The studies on predicting the energy consumption of air conditioning systems are meaningful to building energy conservation and management. Generally, the more comprehensive the building information is, the easier the prediction model can be developed. However, it is very difficult to get detailed information about existing/old buildings (information-poor buildings), it is a big challenge to predict the energy consumption accurately by limited information. This study aims to predict the electricity consumption of the water source heat pump system of an office building based on meteorological data. The key variables are selected by error analysis and sensitivity analysis, and the effects of each variable on the models’ prediction performance can be obtained. Besides, the prediction models are established by support vector regression algorithm and trained by the local meteorological data. The results show that the positive and negative variables can be identified, and these positive variables are responsible for more than 70% of the total importance. Moreover, the root mean square error falls to 4.6044 from 7.8227 and the relative square error falls to 0.1494 from 0.4313 when the negative inputs are removed. And the errors reduce further to 4.1160 and 0.1194 by parameter optimization.

Total Electricity Consumption Forecasting Based on Temperature Composite Index and Mixed-Frequency Models

The total electricity consumption(TEC)can accurately reflect the operation of the national economy,and the forecasting of the TEC can help predict the economic development trend,as well as provide insights for the formulation of macro policies.Nowadays,high-frequency and massive multi-source data provide a new way to predict the TEC.In this paper,a"seasonal-cumulative temperature index"is constructed based on high-frequency temperature data,and a mixed-frequency prediction model based on multi-source big data(Mixed Data Sampling with Monthly Temperature and Daily Temperature index,MIDAS-MT-DT)is proposed.Experimental results show that the MIDAS-MT-DT model achieves higher prediction accuracy,and the"seasonal-cumulative temperature index"can improve prediction accuracy.

融合工况预测的燃料电池汽车里程自适应等效氢耗最小控制策略

为有效地提高插电式燃料电池汽车的经济性,实现燃料电池和动力电池的功率最优分配,考虑到行驶工况、电池荷电状态(State of charge,SOC)、等效因子与氢气消耗之间的密切联系,制定融合工况预测的里程自适应等效氢耗最小策略.通过基于误差反向传播的神经网络来实现未来短期车速的预测,分析未来车辆需求功率变化,同时借助全球定位系统规划一条通往目的地的路径,智能交通系统便可获取整个行程的交通流量信息,利用行驶里程和SOC实时动态修正等效消耗最小策略中的等效因子,实现能量管理策略的自适应性.基于MATLAB/Simulink软件,搭建整车仿真模型与传统的能量管理策略进行仿真对比验证.仿真结果表明,采用基于神经网络的工况预测算法能够较好地预测未来短期工况,其预测精度相较于马尔可夫方法提高12.5%,所提出的能量管理策略在城市道路循环工况(UDDS)下的氢气消耗比电量消耗维持(CD/CS)策略下降55.6%.硬件在环试验表明,在市郊循环工况(EUDC)下的氢气消耗比CD/CS策略下降26.8%,仿真验证结果表明了所提出的策略相比于CD/CS策略在氢气消耗方面的优越性能,并通过硬件在环实验验证了所提策略的有效性.

Genetic algorithm-determined deep feedforward neural network architecture for predicting electricity consumption in real buildings

A genetic algorithm-determined deep feedforward neural network architecture(GA-DFNN)is proposed for both day-ahead hourly and week-ahead daily electricity consumption of a real-world campus building in the United Kingdom.Due to the comprehensive relationship between affecting factors and real-world building electricity consumption,the adoption of multiple hidden layers in the deep neural network(DFNN)algorithm would improve its prediction accuracy.The architecture of a DFNN model mainly refers to its quantity of hidden layers,quantity of neurons in the hidden layers,activation function in each layer and learning process to obtain the connecting weights.The optimal architecture of DFNN model was generally determined through a trial-and-error process,which is an exponential combinatorial problem and a tedious task.To address this problem,genetic algorithm(GA)is adopted to automatically design an optimal architecture with improved generalization ability.One year and six months of measurement data from a campus building is used for training and testing the proposed GA-DFNN model,respectively.To demonstrate the effectiveness of the proposed GA-DFNN prediction model,its prediction performance,including mean absolute percentage error,coefficient of determination,root mean square error and mean absolute error,was compared to the reference feedforward neural network models with single hidden layer,DFNN models with other architecture,random search determined DFNN model,long-short-term-memory model and temporal convolutional network model.The comparison results show that the proposed GA-DFNN predictive model has superior performance than all the reference prediction models,demonstrating the optimization effectiveness of GA and the prediction effectiveness of DFNN model with multiple hidden layers and optimal architecture.

基于Stacking集成学习的无缝钢管连轧电耗预测

无缝钢管生产作为高能耗行业的典型代表,其节能降耗一直都备受关注。通过预测电耗,企业可以找到节能降耗的有效途径,从而减少生产过程中的电能消耗,提升生产效率。为提高无缝钢管连轧电耗预测精度,采用一种改进的Stacking集成学习模型对电耗进行预测。首先,对采集到的电耗数据进行预处理,并基于嵌入法采用XGBoost和LightGBM进行特征选择;然后,采用随机搜索和贝叶斯优化结合的方法对基学习器开展超参数优化,在Stacking集成模型的首层中,选择LightGBM、ET和MLP作为基学习器;最后,根据基学习器在数据上的预测表现来赋予它们相应的权重,同时将原数据集也加入元学习器训练。结果表明:改进的Stacking集成学习模型具有最好的预测效果,其R^(2)为0.975 7,预测精度比单一基学习器和传统的Stacking集成学习模型都要高,证明了所提方法的有效性。

基于规划路径能耗预测的PHEV全局自适应能量管理

新能源汽车智能化能量管理是先进汽车技术研究的重要领域,是进一步提升整车燃油经济性能的关键。针对插电式混合动力汽车(Plug-in hybrid electric vehicle,PHEV)能量全局化管理与控制的实时性和最优性难以兼顾的难题,开展了基于能耗预测的全路径自适应能量管理研究,提出了以等效燃油消耗最小化为目标的全规划路径PHEV自适应控制算法。最后,基于MATLAB/Simulink的建模与仿真分析验证了所提控制算法对实际行驶工况、里程和整车能量状态的变化具有较好的跟随性和自适应性,全路径近似全局性优化控制效果明显,较好地改善了整车的燃油经济性。

基于聚类算法的代理购电工商业用户典型画像分析

对代理购电工商业电力用户用电特征的深入画像分析,将为电力电量预测、发用电计划安排、政策制度完善提供理论依据。本文获取了某地区分行业、分区域的代理购电工商业用户年、月、日等维度的用电数据,同步获取了与之匹配的气象、电价、经济、节假日安排等数据。通过数据处理算法对样本数据剔除离群点、补全缺失值,并建立用电量与温度、经济、用电价格、节假日之间的关联关系,构建全量代理购电工商业用户的特征向量矩阵。利用聚类算法对特征向量矩阵进行聚类分析,并应用划分完成的聚类簇对重点行业用户特征进行画像,画像结果可为实际生产工作提供参考。

Intelligent Energy Utilization Analysis Using IUA-SMD Model Based Optimization Technique for Smart Metering Data

Smart metering has gained considerable attention as a research focus due to its reliability and energy-efficient nature compared to traditional electromechanical metering systems. Existing methods primarily focus on data management,rather than emphasizing efficiency. Accurate prediction of electricity consumption is crucial for enabling intelligent grid operations,including resource planning and demandsupply balancing. Smart metering solutions offer users the benefits of effectively interpreting their energy utilization and optimizing costs. Motivated by this,this paper presents an Intelligent Energy Utilization Analysis using Smart Metering Data(IUA-SMD)model to determine energy consumption patterns. The proposed IUA-SMD model comprises three major processes:data Pre-processing,feature extraction,and classification,with parameter optimization. We employ the extreme learning machine(ELM)based classification approach within the IUA-SMD model to derive optimal energy utilization labels. Additionally,we apply the shell game optimization(SGO)algorithm to enhance the classification efficiency of the ELM by optimizing its parameters. The effectiveness of the IUA-SMD model is evaluated using an extensive dataset of smart metering data,and the results are analyzed in terms of accuracy and mean square error(MSE). The proposed model demonstrates superior performance,achieving a maximum accuracy of65.917% and a minimum MSE of0.096. These results highlight the potential of the IUA-SMD model for enabling efficient energy utilization through intelligent analysis of smart metering data.

融合数据驱动和充电行为的电动汽车能耗预测方法

电动汽车的能耗预测对于车辆路径规划与充电行为至关重要。提出一种考虑充电行为的多模型融合能耗预测方法,首先构建基于实车稀疏数据与有限参数的能耗计算模型,在此基础上构建充电行为模型,分析并提取能耗强相关的充电行为特征,最后基于长短期记忆循环神经网络(Long short-term memory neural network, LSTM)搭建能耗预测模型。使用实车数据对所提方法进行验证,结果表明,该方法可以精准预测相同车型不同起始电池荷电状态(State of charge, SOC)、不同温度、不同时间段下的汽车能耗,均方根误差(Root mean square error,RMSE)为1.27,与现有方法相比,RMSE至少降低4.5%。

“双碳”“双区”背景下电力需求预测方法研究实践

为全面、准确分析新时期发展背景下城市未来用电特征,要结合新形势、新特点,考虑多种新要素,在以往所用方式、方法的基础上进行优化、调整,建立适合“双碳”“双区”发展目标的电力需求预测新体系。该文首先对“双碳”“双区”对城市电力需求预测影响进行分析,并提出碳强度约束下电能占终端能源比重法、细分产业法和新型负荷修正法3种电力需求预测方法,综合预测分析全社会用电量,了解城市中长期电力需求的具体特征,掌握用电总量趋势、用电结构趋势和最高负荷及负荷特性趋势,为城市发展规划顶层设计、控制电力负荷和提高电力投资效益提供参考。

计及匹配偏差分值的用电量周期标签分类方法

用户用电量周期规律复杂多变,各标签之间的相关性不稳定,企业用电量分类精度较低,时间复杂度较大。为提高用电量周期标签分类精度,提出计及匹配偏差分值的用电量周期标签分类方法。根据用电量预测的偏移量收集用电用户的各个维度数据,通过计算用电用户的身份特征属性权重预测用户的用电量。采用匹配偏差分值的计算方式获取用电量的时间序列,根据电力用户的初始特征初步匹配用电用户的行业标签,结合用电量时间序列匹配流程完成用电量时间序列的匹配。基于用户特征的目标函数评估标签位置在排序过程中的移动情况,结合用电量时间序列的匹配偏差分值计算结果设计用电量周期标签算法,实现用电量的周期标签。实验结果表明,所提方法能够根据行业用电量为其标记标签,将行业分类精度和标签与行业之间的匹配度提高到了90%和95%以上,迭代次数达到30时,时间复杂度为2.94 s,提高了用电量周期标签分类精度。

基于改进平衡优化器算法的电力消费预测

针对当前用电预测模型预测精度不足的问题,提出一种基于改进平衡优化器算法的电力消费预测方法。首先,在基本平衡优化器中引入自适应搜索机制,以克服其易陷入局部最优的缺陷;其次,使用自适应平衡优化器调节梯度增强回归器的参数;最后,使用平衡优化器改进的优化梯度增强回归器进行电力消费预测。为了验证所提模型的有效性,使用大规模数据集对所提算法进行了测试。结果表明,所提出的电力消费预测模型具有较好的预测准确性和预测稳定性。