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Adaptive load forecasting of the Hellenic electric grid

Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2008年第12期9卷 1724-1730页

作者： S.Sp.PAPPAS L.EKONOMOU V.C.MOUSSAS P.KARAMPELAS S.K.KATSIKAS Department of Information and Communication Systems Engineering University of the AegeanSamos 83200 Greece Information Technology Faculty Hellenic American UniversityAthens 10680Greece School of Technological Applications Technological Educational Institute of AthensEgaleo 12210Greece Department of Technology Education and Digital Systems University of PiraeusPiraeus 18532Greece

Designers are required to plan for future expansion and also to estimate the grid's future utilization. This means that an effective modeling and forecasting technique, which will use efficiently the information contained in the available data, is required, so that important data properties can be extracted and projected into the future. This study proposes an adaptive method based on the multi-model partitioning algorithm (MMPA), for short-term electricity load forecasting using real data. The grid's utilization is initially modeled using a multiplicative seasonal ARIMA (autoregressive integrated moving average) model. The proposed method uses past data to learn and model the normal periodic behavior of the electric grid. Either ARMA (autoregressive moving average) or state-space models can be used for the load pattern modeling. load anomalies such as unexpected peaks that may appear during the summer or unexpected faults (blackouts) are also modeled. If the load pattern does not match the normal be-havior of the load, an anomaly is detected and, furthermore, when the pattern matches a known case of anomaly, the type of anomaly is identified. Real data were used and real cases were tested based on the measurement loads of the Hellenic Public Power Cooperation S.A., Athens, Greece. The applied adaptive multi-model filtering algorithm identifies successfully both normal periodic behavior and any unusual activity of the electric grid. The performance of the proposed method is also compared to that produced by the ARIMA model.

关键词： Adaptive multi-model filtering ARIMA load forecasting Measurements Kalman filter Order selection Seasonalvariation Parameter estimation

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Building load forecasting Using Deep Neural Network with Efficient Feature Fusion

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Journal of Modern Power Systems and Clean Energy 2021年第1期9卷 160-169页

作者： Jinsong Wang Xuhui Chen Fan Zhang Fangxi Chen Yi Xin the Department of Electrical Computerand System Engineering at Case Western Reserve UniversityClevelandUSA the College of Aeronautics and Engineering Kent State UniversityKentUSA Northeastern University.Shenyang China

The energy consumption of buildings has risen steadily in recent years. It is vital for the managers and owners of the building to manage the electric energy demand of the buildings. forecasting electric energy consumption of the buildings will bring great profits, which is influenced by many factors that make it very difficult to provide an advanced forecasting. Recently, deep learning techniques are widely adopted to solve this problem. Deep neural network offers an excellent capability in handling complex non-linear relationships and competence in exploring regular patterns and uncertainties of consumption behaviors at the building level. In this paper, we propose a deep convolutional neural network based on Res Net for hour-ahead building load forecasting. In addition, we design a branch that integrates the temperature per hour into the forecasting branch. To enhance the learning capability of the model, an innovative feature fusion is presented. At last, sufficient ablation studies are conducted on the point forecasting, probabilistic forecasting, fusion method, and computation *** results show that the proposed model has the state-of-theart performance, which reflects a promising prospect in application of the electricity market.

关键词： load forecasting deep learning convolutional neural network feature fusion Res Net

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Short-term Residential load forecasting Based on K-shape Clustering and Domain Adversarial Transfer Network

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Journal of Modern Power Systems and Clean Energy 2024年第4期12卷 1239-1249页

作者： Jizhong Zhu Yuwang Miao Hanjiang Dong Shenglin Li Ziyu Chen Di Zhang School of Electric Power Engineering South China University of TechnologyGuangzhou 510641China Department of Electrical Engineering The Hong Kong Polytechnic UniversityHong KongChina

In recent years,the expansion of the power grid has led to a continuous increase in the number of consumers within the distribution ***,due to the scarcity of historical data for these new consumers,it has become a complex challenge to accurately forecast their electricity demands through traditional forecasting *** paper proposes an innovative short-term residential load forecasting method that harnesses advanced clustering,deep learning,and transfer learning technologies to address this *** begin,this paper leverages the domain adversarial transfer *** employs limited data as target domain data and more abundant data as source domain data,thus enabling the utilization of source do-main insights for the forecasting task of the target ***,a K-shape clustering method is proposed,which effectively identifies source domain data that align optimally with the target domain,and enhances the forecasting ***,a composite architecture is devised,amalgamating attention mechanism,long short-term memory network,and seq2seq *** composite structure is integrated into the domain adversarial transfer network,bolstering the performance of feature extractor and refining the forecasting *** illustrative analysis is conducted using the residential load dataset of the Independent System Operator to validate the proposed method *** the case study,the relative mean square error of the proposed method is within 30 MW,and the mean absolute percentage error is within 2%.A signifi-cant improvement in accuracy,compared with other comparative experimental results,underscores the reliability of the proposed *** findings unequivocally demonstrate that the proposed method advocated in this paper yields superior forecasting results compared with prevailing mainstream forecast-ing methods.

关键词： load forecasting domain adversarial K-shape clustering long short-term memory network seq2seq network attention mechanism

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Hybrid Short-term load forecasting Method Based on Empirical Wavelet Transform and Bidirectional Long Short-term Memory Neural Networks

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Journal of Modern Power Systems and Clean Energy 2022年第5期10卷 1216-1228页

作者： Xiaoyu Zhang Stefanie Kuenzel Nicolo Colombo Chris Watkins School of Artificial Intelligence Anhui UniversityHefei 230601China Department of Electronic Engineering Royal HollowayUniversity of LondonEgham HillEgham TW20 OEXUK Department of Computer Science Royal HollowayUniversity of LondonEgham HillEgham TW200EXUK

Accurate short-term load forecasting is essential to modern power systems and smart grids. The utility can better implement demand-side management and operate power system stably with a reliable load forecasting system. The load demand contains a variety of different load components, and different loads operate with different frequencies. The conventional load forecasting methods, e.g., linear regression(LR), auto-regressive integrated moving average(ARIMA), deep neural network, ignore the frequency domain and can only use time-domain load demand as inputs. To make full use of both time-domain and frequency-domain features of the load demand, a load forecasting method based on hybrid empirical wavelet transform(EWT) and deep neural network is proposed in this paper. The proposed method first filters noises via wavelet-based denoising technique, and then decomposes the original load demand into several sub-layers to show the frequency features while the time-domain information is preserved as well. Then, a bidirectional long short-term memory(LSTM) method is trained for each sub-layer independently. In order to better tune the hyperparameters, a Bayesian hyperparameter optimization(BHO) algorithm is adopted in this paper. Three case studies are designed to evaluate the performance of the proposed *** the results, it is found that the proposed method improves the prediction accuracy compared with other load forecasting method.

关键词： load forecasting empirical wavelet transform(EWT) recurrent neural network data denoising Bayesian hyperparameter optimization(BHO)

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Personalized Federated Learning for Individual Consumer load forecasting

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CSEE Journal of Power and Energy Systems 2023年第1期9卷 326-330页

作者： Yi Wang Ning Gao Gabriela Hug Department of Electrical and Electronic Engineering The University of Hong KongHong KongChinaare also with ETH ZurichZurichSwitzerland Power Systems Laboratory ETH Zurich8092 ZurichSwitzerland

Installation of smart meters enables electricity retailers or consumers to implement individual load forecasting for demand *** individual load forecasting model can be trained either on each consumer’s own smart meter data or the smart meter data of multiple *** former practice may suffer from overfitting if a complex model is trained because the dataset is limited;the latter practice cannot protect the privacy of individual *** paper tackles the dilemma by proposing a personalized federated approach for individual consumer load ***,a group of consumers first jointly train a federated forecasting model on the shared smart meter data pool,and then each consumer personalizes the federated forecasting model on their own *** case studies are conducted on an open dataset of 100 *** verify the proposed method can enhance forecasting accuracy by making full use of data from other consumers with privacy protection.

关键词： Federated learning load forecasting personalization privacy-preserving smart meter

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Optimal load forecasting Model for Peer-to-Peer Energy Trading in Smart Grids

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Computers, Materials & Continua 2022年第1期70卷 1053-1067页

作者： Lijo Jacob Varghese K.Dhayalini Suma Sira Jacob Ihsan Ali Abdelzahir Abdelmaboud Taiseer Abdalla Elfadil Eisa Department of Electrical and Electronics Engineering Christian College of Engineering and TechnologyOddanchatram624619India Department of Electrical and Electronics Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science and Engineering Christian College of Engineering and TechnologyOddanchatram624619India Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversity of Malaya50603Kuala LumpurMalaysia Department of Information Systems King Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Information Systems-Girls Section King Khalid UniversityMahayil62529Saudi Arabia

Peer-to-Peer(P2P)electricity trading is a significant research area that offers maximum fulfilment for both prosumer and *** also decreases the quantity of line loss incurred in Smart Grid(SG).But,uncertainities in demand and supply of the electricity might lead to instability in P2P market for both prosumer and *** recent times,numerous Machine Learning(ML)-enabled load predictive techniques have been developed,while most of the existing studies did not consider its implicit features,optimal parameter selection,and prediction *** order to overcome fulfill this research gap,the current research paper presents a new Multi-Objective Grasshopper Optimisation Algorithm(MOGOA)with Deep Extreme Learning Machine(DELM)-based short-term load predictive technique i.e.,MOGOA-DELM model for P2P Energy Trading(ET)in *** proposed MOGOA-DELM model involves four distinct stages of operations namely,data cleaning,Feature Selection(FS),prediction,and parameter *** addition,MOGOA-based FS technique is utilized in the selection of optimum subset of ***,DELM-based predictive model is also applied in forecasting the load *** proposed MOGOA model is also applied in FS and the selection of optimalDELM parameters to improve the predictive *** inspect the effectual outcome of the proposed MOGOA-DELM model,a series of simulations was performed using UK Smart Meter *** the experimentation procedure,the proposed model achieved the highest accuracy of 85.80%and the results established the superiority of the proposed model in predicting the testing data.

关键词： Peer to Peer energy trade smart grid load forecasting machine learning feature selection

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An explainable framework for load forecasting of a regional integrated energy system based on coupled features and multi-task learning

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Protection and Control of Modern Power Systems 2022年第1期7卷 349-362页

作者： Kailang Wu Jie Gu Lu Meng Honglin Wen Jinghuan Ma School of Electronic Information and Electrical Engineering Shanghai Jiao Tong UniversityShanghaiChina

To extract strong correlations between different energy loads and improve the interpretability and accuracy for load forecasting of a regional integrated energy system(RIES),an explainable framework for load forecasting of an RIES is *** includes the load forecasting model of RIES and its interpretation.A coupled feature extracting strat-egy is adopted to construct coupled features between loads as the input variables of the *** is designed based on multi-task learning(MTL)with a long short-term memory(LSTM)model as the sharing *** on SHapley Additive exPlanations(SHAP),this explainable framework combines global and local interpretations to improve the interpretability of load forecasting of the *** addition,an input variable selection strategy based on the global SHAP value is proposed to select input feature variables of the model.A case study is given to verify the effectiveness of the proposed model,constructed coupled features,and input variable selection *** results show that the explainable framework intuitively improves the interpretability of the prediction model.

关键词： load forecasting Regional integrated energy system Coupled feature SHapley additive exPlanations Interpretability of deep learning

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Personalized Federated Learning for Heterogeneous Residential load forecasting

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Big Data Mining and Analytics 2023年第4期6卷 421-432页

作者： Xiaodong Qu Chengcheng Guan Gang Xie Zhiyi Tian Keshav Sood Chaoli Sun Lei Cui the Shanxi Key Laboratory of Advanced Control and Equipment Intelligence Taiyuan University of Science and TechnologyTaiyuan 030024China the Faculty of Engineering and Information Technology University of Technology SydneyUltimo 2007Australia the Centre for Cyber Security Research and Innovation Deakin UniversityMelbourne 3125Australia the School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan 030024China

Accurate load forecasting is critical for electricity production,transmission,and *** learning(DL)model has replaced other classical models as the most popular prediction ***,the deep prediction model requires users to provide a large amount of private electricity consumption data,which has potential privacy *** nodes can federally train a global model through aggregation using federated learning(FL).As a novel distributed machine learning(ML)technique,it only exchanges model parameters without sharing raw ***,existing forecasting methods based on FL still face challenges from data heterogeneity and privacy ***,we propose a user-level load forecasting system based on personalized federated learning(PFL)to address these *** obtained personalized model outperforms the global model on local ***,we introduce a novel differential privacy(DP)algorithm in the proposed system to provide an additional privacy *** on the principle of generative adversarial network(GAN),the algorithm achieves the balance between privacy and prediction accuracy throughout the *** perform simulation experiments on the real-world dataset and the experimental results show that the proposed system can comply with the requirement for accuracy and privacy in real load forecasting scenarios.

关键词： load forecasting personalized federated learning differential privacy

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Incremental Learning Model for load forecasting without Training Sample

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Computers, Materials & Continua 2022年第9期72卷 5415-5427页

作者： Charnon Chupong Boonyang Plangklang Faculty of Engineering Rajamangala University of Technology ThanyaburiPathum Thani12110Thailand

This article presents hourly load forecasting by using an incremental learning model called Online Sequential Extreme Learning Machine(OSELM),which can learn and adapt automatically according to new arrival ***,the use of OS-ELM requires a sufficient amount of initial training sample data,which makes OS-ELM inoperable if sufficiently accurate sample data cannot be *** solve this problem,a synthesis of the initial training sample is *** synthesis of the initial sample is achieved by taking the first data received at the start of working and adding random noises to that data to create new and sufficient *** the synthesis samples are used to initial train the *** proposed method is compared with Fully Online Extreme Learning Machine(FOS-ELM),which is an incremental learning model that also does not require the initial training *** the proposed method and FOS-ELM are used for hourly load forecasting from the Hourly Energy Consumption *** have shown that the proposed method with a wide range of noise levels,can forecast hourly load more accurately than the FOS-ELM.

关键词： Incremental learning load forecasting Synthesis data OS-ELM

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Knowledge mining collaborative DESVM correction method in short-term load forecasting

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Journal of Central South University 2011年第4期18卷 1211-1216页

作者：牛东晓王建军刘金朋 School of Economics and Management North China Electric Power University

Short-term forecasting is a difficult problem because of the influence of non-linear factors and irregular events.A novel short-term forecasting method named TIK was proposed,in which ARMA forecasting model was used to consider the load time series trend forecasting,intelligence forecasting DESVR model was applied to estimate the non-linear influence,and knowledge mining methods were applied to correct the errors caused by irregular *** order to prove the effectiveness of the proposed model,an application of the daily maximum load forecasting was *** experimental results show that the DESVR model improves the mean absolute percentage error(MAPE) from 2.82% to 2.55%,and the knowledge rules can improve the MAPE from 2.55% to 2.30%.Compared with the single ARMA forecasting method and ARMA combined SVR forecasting method,it can be proved that TIK method gains the best performance in short-term load forecasting.

关键词： load forecasting support vector regression knowledge mining ARMA differential evolution

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