PoQ-Consensus Based Private Electricity Consumption Forecasting via Federated Learning

With the rapid development of artificial intelligence and computer technology,grid corporations have also begun to move towards comprehensive intelligence and informatization.However,data-based informatization can bring about the risk of privacy exposure of fine-grained information such as electricity consumption data.The modeling of electricity consumption data can help grid corporations to have a more thorough understanding of users’needs and their habits,providing better services for users.Nevertheless,users’electricity consumption data is sensitive and private.In order to achieve highly efficient analysis of massive private electricity consumption data without direct access,a blockchain-based federated learning method is proposed for users’electricity consumption forecasting in this paper.Specifically,a blockchain systemis established based on a proof of quality(PoQ)consensus mechanism,and a multilayer hybrid directional long short-term memory(MHD-LSTM)network model is trained for users’electricity consumption forecasting via the federal learning method.In this way,the model of the MHD-LSTM network is able to avoid suffering from severe security problems and can only share the network parameters without exchanging raw electricity consumption data,which is decentralized,secure and reliable.The experimental result shows that the proposed method has both effectiveness and high-accuracy under the premise of electricity consumption data’s privacy preservation,and can achieve better performance when compared to traditional long short-term memory(LSTM)and bidirectional LSTM(BLSTM).

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.

Hybrid Model Based on Wavelet Decomposition for Electricity Consumption Prediction

The effective supply of electricity is the basis of ensuring economic development and people's normal life. It is difficult to store electricity, as leading to the production and consumption must be completed simultaneously. Therefore, it is of great significance to accurately predict the demand for electricity consumption for the production planning of electricity and the normal operation of the society. In this paper, a hybrid model is constructed to predict the electricity consumption in China. The structural breaks test of monthly electricity consumption in China from January 2010 to December 2016 is carried out by using the structural breaks unit root test. Based on the existence of structura breaks, the electricity consumption data are decomposed into low-frequency and high-frequency components by wavelet model, and the separated low frequency signal and high frequency signal are predicted by autoregressive integrated moving average(ARIMA) and nonlinear autoregressive neural network(NAR), respectively. Therefore the wavelet-ARIMA-NAR hybrid model is constructed. In order to compare the effect of the hybrid model, the structural time series(STS) model is applied to predicting the electricity consumption. The results of prediction error test show that the hybrid model is more accurate for electricity consumption prediction.

Impact of Ambient Temperature on Electricity Demand of Dhaka City of Bangladesh

Per capita electricity consumption of Bangladesh is 400 KWh. Of the total population of 160 million, only 40 percent has the access of using electricity. Dhaka city consumes about 40 - 45 percent of the total electricity generation of the country. This study reports the trend of electricity use in the Dhaka city with emphasis on the impact of changing temperature due to urbanization and weather change. Hourly data of electricity demand of Dhaka city and the temperature profile of the city for a period of thirty months have been used for this study. To relate weather data like temperature, humidity, wind speed, wind direction, atmospheric pressure, dew point and visibility etc. with electricity demand of the city about 16,508 data between 2011 and 2017 have been considered. A statistical regression has been done to establish a relation between them. From this study it is found that reduction of only 1&degC air temperature could save 81 MV of electricity consumption in Dhaka city. A time series forecast has been done to estimate probable temperature change and subsequent electricity consumption up to year 2020. From the study it has been established that the temperature dependence of electricity consumption in Dhaka city is about 75%.

Spatio-temporal Granularity Co-optimization Based Monthly Electricity Consumption Forecasting

Monthly electricity consumption forecasting(ECF)plays an important role in power system operation and electricity market trading.Widespread popularity of smart meters enables collection of fine-grained load data,which provides an opportunity for improvement of monthly ECF accuracy.In this letter,a spatio-temporal granularity co-optimization-based monthly ECF framework is proposed,which aims to find an optimal combination of temporal granularity and spatial clusters to improve monthly ECF accuracy.The framework is formulated as a nested bi-layer optimization problem.A grid search method combined with a greedy clustering method is proposed to solve the optimization problem.Superiority of the proposed method has been verified on a real smart meter dataset.

基于Theil不等系数的IOWA算子和马尔科夫链的电量组合模型研究

通过引进Theil不等系数、IOWA算子和马尔科夫链的概念,建立了一种基于Theil不等系数的IOWA算子和马尔科夫链的电量组合模型。从相关性的角度对该模型进行了研究,同时采用IOWA算子,使组合模型的各个时间点上的权系数只与预测精度有关,与预测方法无关,较好地反映了负荷发展实际情况。利用马尔科夫链定性推出组合模型中各单项模型在待预测时点上的预测精度状态,进而得到待预测时点上的组合模型的权系数。以陕西省某市1994～2009年年用电量为样本,通过指数平滑法、回归法和灰色模型法分别建立模型,然后利用基于Theil不等系数的IOWA算子和马尔科夫链的组合模型进行权系数的求解,实证分析表明该模型使预测精度得到了明显提高,具有良好的预测效果。

基于边缘计算的商业区电力能源用电量动态预测系统

商业区电力能源内部拥有海量电力数据,动态预测能源用电量可以有效改善系统业务,确保用户拥有较好的用电体验。针对目前预测系统准确率较低这一问题,文中基于边缘计算设计了一种新的商业区电力能源用电量动态预测系统。在硬件设计上,利用AC/DC模块将交流电转化为直流电,运用电动机控制器连接被测量电动机与商业区供电,连接传动轴与供电测功机,实现电流传递。利用测功机控制器对区域电网动力电进行管理和控制。在软件设计上,采用边缘计算的方式对区域内电力能源进行分析,模拟构建电量动态预测程序,根据动态预测程序实现用电量预测。实验结果表明,该系统在短期预测和长期预测方面,都具有良好的能力,输出值与真实值相关系数为0.99,预测误差低于0.55%。

基于最优化残差划分Markov修正的城市用电量预测模型

对湖州城市居民用电量进行预测过程中,历史用电量数据显示出较强的波动性与季节性,导致原始模型预测效果不理想,文章引入并改进Markov修正组合模型,将Markov修正残差划分部分改进为不同算子残差划分,并用来修正新陈代谢GM(1,1)、SARIMA、Holt-Winters、LSTM等原始模型。使用DC-Markov、MC-Markov、SC-Markov修正后的组合模型预测湖州市的未来月份城市居民用电数据。结果表明,文章提出的最优化残差划分Markov修正模型预测精度较原始模型有一定程度提高,DC-Markov-Holt-Winters模型在湖州市城市居民用电数据的预测上具有较高的精度。

基于气象因子的EEMD-BP方法在电网用电量预测中的应用

[目的]随着风能、太阳能等清洁能源快速发展,电力系统的能源结构发生了重大变化,这使得电网安全运行的不确定性增大,也给精准用电量预测带来了新的挑战。电网用电量受众多因子的影响,而气象因子的影响显著,因此,分析气象因子对用电量精细化预测的影响显得尤为重要。[方法]利用2017年逐日用电量以及最高气温、平均气温、最低气温、气压、相对湿度、风速等气象数据,采用集合模态经验分解(EEMD)和BP神经网络组合预测方法,探讨气象因子对集合模态经验分解回归模型(EEMD-BP)方法预测用电量的影响。[结果]研究发现,平均气温、最高气温、最低气温、气压和相对湿度与用电量序列经EEMD分解后的低频分量存在较好的相关关系,而与高频分量和周期分量的相关性较弱。[结论]利用BP回归模型预测的用电量与实况误差较大,引进气象因子后,EEMD-BP得出的预测准确率有了明显的提高。研究表明,基于气象因子的EEMD-BP组合预测方法可有效提高用电量预测的准确率,可为完善短期用电量预测方法提供有效的技术支撑。

考虑数据分类的建筑电能耗集成预测方法

建筑侧各类可再生能源的应用日益普及,建筑电能耗预测在用能供需平衡、电网稳定运行、尖峰需求响应等方面发挥越来越重要作用。尽管诸多数据驱动模型在能耗预测方面获得广泛应用,当前仍缺乏预测精度高、泛化能力强的短期预测模型。针对该问题,提出一种基于建筑物能耗特点并结合数据挖掘技术的分类集成式能耗预测方法。首先,采用递归特征消除法对数据进行特征筛选,并用模糊C均值聚类算法对训练集数据进行聚类,使用K最邻近法对验证集和测试集数据进行归类;选择5种结合智能优化算法的混合数据驱动模型作为子学习器,分别对每类数据做预测,最后使用多元线性回归法进行结果集成。经3个建筑电力用能案例验证,此集成预测模型精度均优于单个子模型,具有适用不同建筑类型和用能尺度的预测潜力。

计及用电行为模式的区域商业建筑负荷预测方法

为充分利用智能电表采集的细粒负荷数据并提高区域商业建筑负荷预测的精确度,提出一种基于用电行为模式的区域商业建筑负荷预测方法。首先,基于均值方差归一化方法对采集到的负荷数据进行标准化处理,通过肘方法确定聚类数目后进行k-Shape聚类,实现区域商业建筑负荷不同用电行为模式的提取;其次,针对大规模商业建筑负荷预测问题,考虑区域内大量商业建筑负荷预测时耗费大量内存资源却难以实现较准确预测问题,提出一种改进的Informer模型,该模型通过聚类算法识别具有相似用电行为模式的商业建筑,并充分考虑智能电表采集的异常负荷数据对模型训练结果的影响,能够良好的解决大规模商业建筑负荷预测精度不高问题;最后,采用加利福尼亚州商业建筑负荷进行实验,实验结果表明所提方法能够有效提高区域商业建筑负荷预测精度。

A self-adaptive grey forecasting model and its application

GM(1,1)models have been widely used in various fields due to their high performance in time series prediction.However,some hypotheses of the existing GM(1,1)model family may reduce their prediction performance in some cases.To solve this problem,this paper proposes a self-adaptive GM(1,1)model,termed as SAGM(1,1)model,which aims to solve the defects of the existing GM(1,1)model family by deleting their modeling hypothesis.Moreover,a novel multi-parameter simultaneous optimization scheme based on firefly algorithm is proposed,the proposed multi-parameter optimization scheme adopts machine learning ideas,takes all adjustable parameters of SAGM(1,1)model as input variables,and trains it with firefly algorithm.And Sobol’sensitivity indices are applied to study global sensitivity of SAGM(1,1)model parameters,which provides an important reference for model parameter calibration.Finally,forecasting capability of SAGM(1,1)model is illustrated by Anhui electricity consumption dataset.Results show that prediction accuracy of SAGM(1,1)model is significantly better than other models,and it is shown that the proposed approach enhances the prediction performance of GM(1,1)model significantly.

Predicting Electric Energy Consumption for a Jerky Enterprise

Wholesale and retail markets for electricity and power require consumers to forecast electricity consumption at different time intervals. The study aims to increase economic efficiency of the enterprise through the introduction of algorithm for forecasting electric energy consumption unchanged in technological process. Qualitative forecast allows you to essentially reduce costs of electrical energy, because power cannot be stockpiled. Therefore, when buying excess electrical power, costs can increase either by selling it on the balancing energy market or by maintaining reserve capacity. If the purchased power is insufficient, the costs increase is due to the purchase of additional capacity. This paper illustrates three methods of forecasting electric energy consumption: autoregressive integrated moving average method, artificial neural networks and classification and regression trees. Actual data from consuming of electrical energy was used to make day, week and month ahead prediction. The prediction effect of prediction model was proved in Statistica simulation environment. Analysis of estimation of the economic efficiency of prediction methods demonstrated that the use of the artificial neural networks method for short-term forecast allowed reducing the cost of electricity more efficiently. However, for mid- range predictions, the classification and regression tree was the most efficient method for a Jerky Enterprise. The results indicate that calculation error reduction allows decreases expenses for the purchase of electric energy.

基于数据挖掘的电采暖电量预测及应用

为简便实现电采暖电量预测,提出了基于数据挖掘的电量预测方法。该方法对电采暖电量和众多影响因素进行灰色关联分析,筛选出与电量有强关联的因素;利用线性回归算法构建了采暖用户预测模型,根据平均采暖户和权重系数实现对电采暖电量准确快速的预测,并创建了预测分析小工具,实现了电量的可视化预测分析。对河北某地的电量实测数据进行验证,与双向LSTM网络预测结果对比,该预测方法效果较好,同时模型的参数少且计算时间短,简化了电采暖电量预测方法。

Month ahead average daily electricity price profile forecasting based on a hybrid nonlinear regression and SVM model:an ERCOT case study

With the deregulation of the electric power industry, electricity price forecasting plays an increasingly important role in electricity markets, especially for retailors and investment decision making. Month ahead average daily electricity price profile forecasting is proposed for the first time in this paper. A hybrid nonlinear regression and support vector machine(SVM) model is proposed. Offpeak hours, peak hours in peak months and peak hours in off-peak months are distinguished and different methods are designed to improve the forecast accuracy. A nonlinear regression model with deviation compensation is proposed to forecast the prices of off-peak hours and peak hours in off-peak months. SVM is adopted to forecast the prices of peak hours in peak months. Case studies based on data from ERCOT validate the effectiveness of the proposed hybrid method.

基于BP神经网络的芜湖市社会用电量预测研究

为通过社会用电量分析来反应经济状况,文章采用BP神经网络算法,首先对芜湖市近几年的社会用电量进行分析,再通过Matlab神经网络工具箱构建预测—时间序列模型,最后根据需求获得相应的预测数据。仿真试验表明,该模型找到一条函数曲线很好地拟合已知数据,同时顺利地预测未知数据,所提算法达到预期的效果。

改进随机森林算法在用电预测中的应用

全社会用电量对于电力企业的经营和管理具有重要作用。提高全社会用电量的预测精度,有利于合理调配电力资源,提前为“迎峰度夏”等特定用电场景作好供电准备。针对全社会用电预测难度较大的问题,提出利用K-means聚类对行业用电数据进行有效区分。根据不同类型行业的波动特点,采用季节性自回归整合滑动平均(SARIMA)和随机森林(RF)的混合模型预测出各类型行业的用电指数,以预测全社会用电量发展趋势,从而提高预测准确率。以某省2018年1月至2021年6月全社会及各行业月用电量数据作为样本数据,测算发现各行业用电波动有明显差异。研究结果显示:该模型能够对不同类型行业的用电特点进行修正,具有较好的稳定性;全社会用电量的预测结果准确,相对误差控制在2.0%以下。

基于用电采集数据的居民需求响应分析技术研究

居民需求响应是一种通过调整用电行为来实现能源节约和环境保护的策略,而基于用电采集数据的居民需求响应分析技术则可以更好地解决居民的用电需求,并为制定有针对性的策略和措施提供支持。为此,本文重点探讨了基于用电采集数据的居民需求响应分析技术。该技术通过采集居民用电数据,深入分析居民用电习惯、高峰用电时段以及用电量的变化趋势等数据,可以识别出潜在的节能机会和需求响应的潜力。同时,通过模拟和预测小区不同负荷下的粒度和精度,为电力部门制定精确的政策和措施提供科学依据。

湖北省电力消费与经济增长关系研究及发展预测

“十三五”以来,湖北加快产业结构调整和新旧动能转换,经济增长和电力消费的协调性快速调整,关系更加紧密。本文通过从多个角度分析湖北省“十三五”以来经济增长和电力消费的特点和规律,研究经济增长和电力消费的相关关系,在此基础上结合湖北省“十四五”规划目标,对“十四五”末湖北省电力消费相关指标进行预测,从而为湖北能源发展和经济发展科学决策提供重要参考和依据。根据预测结果,“十四五”期间,湖北省电力消费将保持较快增长,全社会用电量年均增速将高于GDP年均增速。

基于Simple Seasonal模型上网电量和厂用电量的发电量预测

近年来,预测发电量统计引起国家电网的高度关注。供电企业对发电量进行预测,不仅能满足社会用电要求,还能提高企业的竞争力。文章以辽宁省的两个火电厂为例,采用Simple Seasonal模型预测未来1个月的上网电量和厂用电量,进而统计发电量的预测值。在确保电网安全前提下,不仅可以保障电力供应平稳有序,最大限度减少因无序用电带来的损失,还可以为节约用电开辟一条新的道路。