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.

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.

A comprehensive review for wind,solar,and electrical load forecasting methods

Wind power,solar power,and electrical load forecasting are essential works to ensure the safe and stable operation of the electric power system.With the increasing permeability of new energy and the rising demand response load,the uncertainty on the production and load sides are both increased,bringing new challenges to the forecasting work and putting forward higher requirements to the forecasting accuracy.Most review/survey papers focus on one specific forecasting object(wind,solar,or load),a few involve the above two or three objects,but the forecasting objects are surveyed separately.Some papers predict at least two kinds of objects simultaneously to cope with the increasing uncertainty at both production and load sides.However,there is no corresponding review at present.Hence,our study provides a comprehensive review of wind,solar,and electrical load forecasting methods.Furthermore,the survey of Numerical Weather Prediction wind speed/irradiance correction methods is also included in this manuscript.Challenges and future research directions are discussed at last.

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.

Short-Term Electricity Price Forecasting Using a Combination of Neural Networks and Fuzzy Inference

This paper presents an artificial neural network, ANN, based approach for estimating short-term wholesale electricity prices using past price and demand data. The objective is to utilize the piecewise continuous na-ture of electricity prices on the time domain by clustering the input data into time ranges where the variation trends are maintained. Due to the imprecise nature of cluster boundaries a fuzzy inference technique is em-ployed to handle data that lies at the intersections. As a necessary step in forecasting prices the anticipated electricity demand at the target time is estimated first using a separate ANN. The Australian New-South Wales electricity market data was used to test the system. The developed system shows considerable im-provement in performance compared with approaches that regard price data as a single continuous time se-ries, achieving MAPE of less than 2% for hours with steady prices and 8% for the clusters covering time pe-riods with price spikes.

Overview of the Global Electricity System in Oman Considering Energy Demand Model Forecast

Lately,in modern smart power grids,energy demand for accurate forecast of electricity is gaining attention,with increased interest of research.This is due to the fact that a good energy demand forecast would lead to proper responses for electricity demand.In addition,proper energy demand forecast would ensure efficient planning of the electricity industry and is critical in the scheduling of the power grid capacity and management of the entire power network.As most power systems are been deregulated and with the rapid introduction and development of smart-metering technologies in Oman,new opportunities may arise considering the efficiency and reliability of the power system;like price-based demand response programs.These programs could either be a large scale for household,commercial or industrial users.However,excellent demand forecasting models are crucial for the deployment of these smart metering in the power grid based on good knowledge of the electricity market structure.Consequently,in this paper,an overview of the Oman regulatory regime,financial mechanism,price control,and distribution system security standard were presented.More so,the energy demand forecast in Oman was analysed,using the econometric model to forecasts its energy peak demand.The energy econometric analysis in this study describes the relationship between the growth of historical electricity consumption and macro-economic parameters(by region,and by tariff),considering a case study of Mazoon Electricity Distribution Company(MZEC),which is one of the major power distribution companies in Oman,for effective energy demand in the power grid.

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.

Influences of uncertainties to the generation feasible region for medium- and long-term electricity transaction

For the implementation of power market in China,medium-and Iong-term security checks are essential for bilateral transactions,of which the electricity quantity that constitutes the generation feasible region(GFR)is the target.However,uncertainties from load forecasting errors and transmission contingencies are threats to medium-and Iong-term electricity tradi ng in terms of their in flue nces on the GFR.In this paper,we prese nt a graphic distortio n pattern in a typical threegenerator system using the Monte Carlo method and projection theory based on security constrained economic dispatch.The underlying potential risk to GFR from uncertainties is clearly visualized,and their impact characteristics are discussed.A case study on detailed GFR distortion was included to dem on strate the effectiveness of this visualization model.The result implies that a small uncertainty could distort the GFR to a remarkable extent and that different line-contingency precipitates disparate the GFR distortion patterns,thereby eliciting great emphasis on load forecasting and line reliability in electricity transacti ons.

Seasonal electric vehicle forecasting model based on machine learning and deep learning techniques

In this paper,multiple featured machine learning algorithms and deep learning algorithms are applied in fore-casting the electric vehicles charging load profile from real datasets of Spain’s electrical grid.The study aims to provide realistic datasets of electric vehicle load profiles to cope with the potential increase in electric vehicle penetration taking into consideration the seasonality effects.Technical issues are caused by the distribution network of the electricity grid;such as the huge charging power and stochastic charging behaviors of the drivers of electric vehicles due to the mass rollout of electric vehicles.Forecasting electric vehicles’load profile is necessary to face challenges to solve the problem of the potential mass rollout of electric vehicles penetration.However,Electric vehicle is considered one of the most promising solutions that develops faster than other stochastic renewable solution to reduce greenhouse emissions.The seasonality effect is one of the huge chal-lenges on electrical loads,so it is investigated by creating four separate forecasting networks to increase system accuracy and studying the effect of seasonal factors such as temperature fluctuation in the four seasons affecting the electric vehicles’battery in charging and draining modes.These factors are affecting the accuracy of the forecasting model.Four featured algorithms are investigated.Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference Systems are applied as machine learning algorithms,and Long Short-Term Memory and the Gated Recurrent Units are also utilized as deep learning algorithms.The Gated Recurrent Units model performs slightly better than the long short-term memory employed on the hourly average daily historical data of charging electric vehicles.While the Adaptive Neuro-Fuzzy Inference System gathers both Artificial Neural Network and Fuzzy Inference System advantages.

园区受端新型电力系统电力电量再平衡方法

我国工业园区电能消耗占据了较高的比例,针对依托新型电力系统促进工业园区绿色低碳发展的需要,提出了一种面向工业园区受端新型电力系统的电力电量再平衡方法。首先,开展电力和电量需求预测并进行电力电量初平衡;然后,基于受端源网荷储协同作用并充分考虑园区节能、电能替代、各类分布式电源、储能和需求响应能力的作用进行电力电量再平衡,由此确定园区年度外调电和区内自产电的比例,进一步建立包含低碳效应和电力系统规模变化在内的量化指标评价体系,对电力电量再平衡带来的变配电容量缩减规模和降碳效用进行评价。以我国南方某工业园区新型电力系统的电力电量再平衡为例对以上方法进行了验证,结果表明:该园区2030年变配电规划容量可缩减10.1%,用电综合碳排放因子由0.60 kg/(kW·h)降至0.54 kg/(kW·h);2060年变配电规划容量可缩减9.57%,电能替代实现减碳5.85万t/a,可为受端新型电力系统的电力电量平衡提供有力的理论支撑。

基于Prophet-XGBoost组合模型的极端温度事件下负荷预测

气候变化对城市的影响日益加剧,频发的极端温度事件导致城市电力系统供需不平衡问题凸显,精确的需求侧电力负荷预测成为提升电力系统适应性从而支持城市功能稳定性的关键。本文开发了一种适用于极端温度事件下负荷预测的组合模型,结合时间序列模型Prophet和机器学习模型XGBoost,有效表征极端温度影响下的电力负荷波动趋势。实验结果表明,相比传统单一模型,组合模型显著提高了极端温度事件下的电力负荷预测精度,在增强城市电力系统对气候变化适应性方面具有较强的有效性,从而为电力调度等电力系统应急管理工作提供了更可靠的支持。

多模态马尔科夫决策泛在电力物联网大数据智能挖掘

针对泛在电力物联网结构复杂、数据多样且不确定的问题,提出了一种基于多模态马尔科夫决策的泛在电力物联网大数据智能挖掘方法。该方法构建了一种基于最大熵的马尔科夫决策算法,对电力泛在物联网进行故障诊断和负荷预测,具有标记样本需求量小、置信度高的特点。通过结合电气量信息及开关量信息来提取电网数据特征,从而充分利用多模态数据样本。仿真分析与实验结果表明,相比于传统方法,所提方法能够有效识别出包括信息畸变在内的电网故障,提升电网故障诊断的准确率和电网负荷预测的精度。

电力工程中的电力负荷预测与调度优化研究

电力负荷预测和调度优化是电力工程中的重要研究领域,对于确保电力系统的稳定运行具有关键作用。基于此,本文介绍电力负荷预测的基本概念和目标,探讨电力负荷预测的方法,常见负荷预测方法主要包括时间序列分析、回归分析、人工神经网络、支持向量机等。这些方法可根据不同数据特点和预测需求,选择合适的模型进行负荷预测。根据实践研究证明,电力负荷预测与调度优化是电力工程中的重要研究内容,通过负荷预测和调度优化,能有效提高电力供应质量,为电力系统可持续发展做出贡献。

基于光纤通信技术的电网规划电力负荷预测系统设计

文章采用光纤通信、分布式大数据平台、长短期记忆(Long Short-Term Memory,LSTM)网络以及XGBoost算法等先进技术手段,从数据采集传输、存储管理、负荷预测建模以及结果展示交互等方面,全面设计和实现电力负荷预测系统。实验结果表明,该系统能够在保证数据传输可靠性的同时,显著提高负荷预测的精度,证实该系统在实际电网应用中具有可行性和有效性,能够为电网规划和运行管理提供有力支撑。

Novel grey forecast model and its application

The advancement of grey system theory provides an effective analytic tool for power system load fore-cast. All kinds of presently available grey forecast models can be well used to deal with the short-term load fore-cast. However, they make big errors for medium or long-term load forecasts, and the load that does not satisfythe approximate exponential increasing law in particular. A novel grey forecast model that is capable of distin-guishing the increasing law of load is adopted to forecast electric power consumption (EPC) of Shanghai. Theresults show that this model can be used to greatly improve the forecast precision of EPC for a secondary industryor the whole society.

Unbalance Level Regulating Algorithm in Power Distribution Networks

The paper dwells on the unified power quality indexes characterizing the phenomenon of voltage unbalance in three-phase systems. Voltage unbalance is one of the commonest occurrences in the town mains of 0.38 kV voltage. The phenomenon describes as inequality of vector magnitude of phase voltage and shearing angle between them. Causes and consequences of the voltage unbalance in distribution networks have been considered. The algorithm, which allows switching one-phase load, has been developed as one of the methods of reducing the unbalance level. The algorithm is written in the function block diagram programming language. For determining the duration and magnitude of the unbalance level it is proposed to introduce the forecasting algorithm. The necessary data for forecasting are accumulated in the course of the algorithm based on the Function Block Diagram. The algorithm example is given for transforming substation of the urban electrical power supply system. The results of the economic efficiency assessment of the algorithm implementation are shown in conclusion. The use of automatic switching of the one-phase load for explored substation allows reducing energy losses (active electric energy by 7.63%;reactive energy by 8.37%). It also allows improving supply quality to a consumer. For explored substation the average zero-sequence unbalance factor has dropped from 3.59% to 2.13%, and the negative-sequence unbalance factor has dropped from 0.61% to 0.36%.

V2G应用进展综述

从电网与电动汽车用户2个角度出发,介绍了国内外V2G技术应用现状,分析了基于电网侧V2G技术带来的负面影响以及负荷预测的变化;阐述了激励电动汽车用户参与V2G的两大因素:一是分时电价,二是电池损耗,并提出了引导V2G的构想;探讨了未来EV在可再生能源和虚拟电厂方面的应用;总结了V2G在实际应用方面的难点,提出了展望。

分时电价下电动汽车参与虚拟电厂的经济优化调度方法

随着物联网以及智慧电网的迅速发展,供需平衡中需求侧资源的作用逐渐增大。然而,大量无序应用需求响应技术会对配电网的运行可靠性产生影响。针对此类问题,提出了电动汽车基线负荷预测与负荷削减、负荷转移两种激励型需求响应策略相结合的虚拟电厂经济优化调度方法。首先,根据历史数据采用三次指数平滑法完成风/光电站出力及电动汽车基线负荷的数据预测,观测电力用户的可调能力;然后,基于分时电价机制以虚拟电厂经济最优为目标,增加功率平衡、风/光新能源预测出力和储能系统运行成本等系统约束条件,建立虚拟电厂经济最优调度模型;最后,以河南郑州某地5个电动汽车充电站和风光电站等实际数据验证所提出方法的精准性和有效性。

Short-Term Wind Power Prediction Method Based on Combination of Meteorological Features and CatBoost

As one of the hot topics in the field of new energy,short-term wind power prediction research should pay attention to the impact of meteorological characteristics on wind power while improving the prediction accuracy.Therefore,a short-term wind power prediction method based on the combination of meteorological features and Cat Boost is presented.Firstly,morgan-stone algebras and sure independence screening(MS-SIS)method is designed to filter the meteorological features,and the influence of the meteorological features on the wind power is explored.Then,a sort enhancement algorithm is designed to increase the accuracy and calculation efficiency of the method and reduce the prediction risk of a single element.Finally,a prediction method based on Cat Boost network is constructed to further realize short-term wind power prediction.The National Renewable Energy Laboratory(NREL)dataset is used for experimental analysis.The results show that the short-term wind power prediction method based on the combination of meteorological features and Cat Boost not only improve the prediction accuracy of short-term wind power,but also have higher calculation efficiency.

基于CEMD-CNN-LSTM的中长期电力负荷预测

针对诸多复杂因素影响电力负荷在中长期运行阶段中的预测准确度的问题,提出一种卷积神经网络(CNN)与长短期记忆网络(LSTM)混合的预测算法,从而达到优化负荷预测性能的目的。CNN-LSTM混合预测算法利用模态分解法将负荷数据进行分解,并将其转化为本征模态分量IMF及残差两个部分。同时,引入k均值聚类方法,确定最优聚类标签,搭建神经网络并完成数据输入。在形成特征向量的过程中,运用神经网络挖掘数据间的各类特征并进行预测。最后,采用线性相加的形式处理预测结果,获取预测负荷。仿真结果表明了CNN-LSTM混合预测算法在预测速度与精度上的性能更为优越。