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ردیف | عنوان | نوع |
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1 |
Federated learning with hyperparameter-based clustering for electrical load forecasting
یادگیری فدرال با خوشهبندی مبتنی بر فراپارامتر برای پیشبینی بار الکتریکی-2022 Electrical load prediction has become an integral part of power system operation. Deep learning
models have found popularity for this purpose. However, to achieve a desired prediction
accuracy, they require huge amounts of data for training. Sharing electricity consumption data
of individual households for load prediction may compromise user privacy and can be expensive
in terms of communication resources. Therefore, edge computing methods, such as federated
learning, are gaining more importance for this purpose. These methods can take advantage of
the data without centrally storing it. This paper evaluates the performance of federated learning
for short-term forecasting of individual house loads as well as the aggregate load. It discusses the
advantages and disadvantages of this method by comparing it to centralized and local learning
schemes. Moreover, a new client clustering method is proposed to reduce the convergence time
of federated learning. The results show that federated learning has a good performance with a
minimum root mean squared error (RMSE) of 0.117 kWh for individual load forecasting.
Keywords: Federated learning | Electricity load forecasting | Edge computing | LSTM | Decentralized learning |
مقاله انگلیسی |
2 |
Designing a short-term load forecasting model in the urban smart grid system
طراحی یک مدل پیش بینی بار کوتاه مدت در سیستم شبکه هوشمند شهری-2020 The transition of the energy system from fossil fuel towards renewable energy (RE) is rising sharply, which
provides a cleaner energy source to the urban smart grid system. However, owing to the volatility and intermittency
of RE, it is challenging to design an accurate and reliable short-term load forecasting model. Recently,
machine learning (ML) based forecasting models have been applied for short-term load forecasting whereas most
of them ignore the importance of characteristics mining, parameters fine-tuning, and forecasting stability. To
dissolve the above issues, a short-term load forecasting model is proposed that incorporates thorough data
mining and multi-step rolling forecasting. To alleviate the chaos of short-term load, a de-noising method based
on decomposition and reconstruction is used. Then, a phase space reconstruction (PSR) method is employed to
dynamically determine the train-test ratios and neurons settings of the artificial neural network (ANN). Further,
a multi-objective grasshopper optimization algorithm (MOGOA) is applied to optimize the parameters of ANNs.
Case studies are conducted in the urban smart grid systems of Victoria and New South Wales in Australia.
Simulation results show that the proposed model can forecast short-term load well with various measurement
metrics. Multiple criterion and statistical evaluation also show the good performance of the proposed forecasting
model in terms of accuracy and stability. To conclude, the proposed model achieves high accuracy and robustness,
which will provide references to RE transitions and smart grid optimization, and offer guidance to
sustainable city development. Keywords: Smart grid | Short-term load forecasting | Neural networks | Multi-objective optimization algorithm | Urban sustainability |
مقاله انگلیسی |
3 |
Deployment of data-mining short and medium-term horizon cooling load forecasting models for building energy optimization and management
استقرار مدل های پیش بینی خنک کننده افقی کوتاه مدت و میان مدت برای داده کاوی برای بهینه سازی و مدیریت انرژی ساختمان-2019 In this study, data-mining techniques comprising three forecasting algorithms for accurate and precise cooling load requirement prediction in the building environment, with the primary aim and the objective of improving the load management are applied. Three state-of-the-art cooling load prediction algorithms are –multiple-linear regression (MLR) model, Gaussian process regression (GPR) model and Levenberg–Marquardt backpropagation neural network (LMB-NN) model. The Pearson correlation analysis is prac- ticed calculating the correlation between actual cooling load demand and input feature variables of cli- mate parameters. The impact of climate variability on the building load requirement is also analyzed. Forecasting intervals are divided into two basic parts: (i) 7-day ahead prediction; and (ii) 1-month ahead prediction. To assess the prediction performance, four performance evaluation indices are applied, which are: (i) coefficient of correlation ( R ); (ii) mean absolute error (MAE); (iii) mean absolute percentage error (MAPE); and (iv) coefficient of variation (CV). The model’s performance is compared with the selection of different hidden neurons at different load conditions. The MAPE for 7-day ahead prediction interval by MLR, GPR and LMB-NN model is 13.053%, 0.405% and 2.592%, respectively. Furthermore, the data-mining algorithms are compared and validated with the previous study, and the MAPE of Bayesian regularization neural networks is calculated 2.515% for 7-day ahead prediction. It was witnessed that the algorithms could be applied to facilitate the building cooling load prediction, by applying a relatively limited num- ber of parameters related to energy usage as well as environmental impact in the building environment. The forecasting results show that the three algorithms are effective in predicting the irregular behavior in the data as well as cooling load demand prediction Keywords: Water source heat pump | Data mining models | Cooling load prediction | Building load |
مقاله انگلیسی |
4 |
Day-ahead building-level load forecasts using deep learning vs: traditional time-series techniques
پیش بینی سطح بار ساختمان در سطح روز با استفاده از یادگیری عمیق در مقابل تکنیک های سری زمانی سنتی-2019 Load forecasting problems have traditionally been addressed using various statistical methods, among which
autoregressive integrated moving average with exogenous inputs (ARIMAX) has gained the most attention as a
classical time-series modeling method. Recently, the booming development of deep learning techniques make
them promising alternatives to conventional data-driven approaches. While deep learning offers exceptional
capability in handling complex non-linear relationships, model complexity and computation efficiency are of
concern. A few papers have explored the possibility of applying deep neural networks to forecast time-series load
data but only limited to system-level or single-step building-level forecasting. This study, however, aims at filling
in the knowledge gap of deep learning-based techniques for day-ahead multi-step load forecasting in commercial
buildings. Two classical deep neural network models, namely recurrent neural network (RNN) and convolutional
neural network (CNN), have been proposed and formulated under both recursive and direct multi-step manners.
Their performances are compared with the Seasonal ARIMAX model with regard to accuracy, computational
efficiency, generalizability and robustness. Among all of the investigated deep learning techniques, the gated 24-
h CNN model, performed in a direct multi-step manner, proves itself to have the best performance, improving the
forecasting accuracy by 22.6% compared to that of the seasonal ARIMAX. Keywords: Time-series building-level load forecasts | Deep learning | Gating mechanism | Seasonal ARIMAX |
مقاله انگلیسی |
5 |
Deep ensemble learning based probabilistic load forecasting in smart grids
پیش بینی بار احتمالی مبتنی بر یادگیری گروه عمیق در شبکه های هوشمند-2019 With the availability of fine-grained smart meter data, there has been increasing interest in using this information for ecient and
reliable energy management. In particular, accurate probabilistic load forecasting for individual consumers is critical in determining
the uncertainties in future demand with the goal of improving smart grid reliability. Compared with the aggregate loads, individual
load profiles exhibit higher irregularity and volatility and thus less predictable. To address these challenges, a novel deep ensemble
learning based probabilistic load forecasting framework is proposed to quantify the load uncertainties of individual customers.
This framework employs the profiles of dierent customer groups integrated into the understanding of the task. Specifically,
customers are clustered into separate groups based on their profiles and multitask representation learning is employed on these
groups simultaneously. This leads to a better feature learning across groups. Case studies conducted on an open access dataset from
Ireland demonstrate the eectiveness and superiority of the proposed framework Keywords: Deep ensemble learning | multitask representation learning | probabilistic load forecasting | smart grid | customer profiles |
مقاله انگلیسی |
6 |
Compression of smart meter big data_ A survey
فشرده سازی داده های بزرگ متریک هوشمند : یک مرور-2018 In recent years, the smart grid has attracted wide attention from around the world. Large scale data are collected
by sensors and measurement devices in a smart grid. Smart meters can record fine-grained information about
electricity consumption in near real-time, thus forming the smart meter big data. Smart meter big data has
provided new opportunities for electric load forecasting, anomaly detection, and demand side management.
However, the high-dimensional and massive smart meter big data not only creates great pressure on data
transmission lines, but also incur enormous storage costs on data centres. Therefore, to reduce the transmission
pressure and storage overhead, improve data mining efficiency, and thus fulfil the potential of smart meter big
data. This study presents a comprehensive study on the compression techniques for smart meter big data. The
development of smart grids and the characteristics and application challenges of electric power big data are first
introduced, followed by analysis of the characteristics and benefits of smart meter big data. Finally, this study
focuses on the potential data compression methods for smart meter big data, and discusses the evaluation
methods for smart meter big data compression.
Keywords: Smart grid ، Smart meter ، Energy big data ، Data compression |
مقاله انگلیسی |
7 |
فشرده سازی هوشمند برای داده های بزرگ: مرور
سال انتشار: 2018 - تعداد صفحات فایل pdf انگلیسی: 11 - تعداد صفحات فایل doc فارسی: 40 در سال های اخیر، شبکه هوشمند توجه گسترده ای از سراسر جهان را به خود جلب کرده است. داده های مقیاس بزرگ توسط سنسور ها و دستگاه های اندازه گیری در یک شبکه هوشمند جمع آوری می شوند. مقیاس هوشمند می تواند اطلاعات دقیق در مورد مصرف الکتریسیته را در زمان واقعی به ثبت برساند، بنابراین داده های بزرگ در مقیاس هوشمند اندازه گیری می شود. داده های بزرگ مقیاس هوشمند فرصت های جدیدی برای پیش بینی بار الکتریکی، کشف عادت ها و مدیریت تقاضا ارائه داده است. با این حال، ابعاد بزرگ و داده های بزرگ در مقیاس هوشمند عظیم نه تنها فشار زیادی را بر خطوط انتقال داده ایجاد می کند، بلکه هزینه های ذخیره سازی زیادی را در مراکز داده نیز به همراه می آورد. بنابراین، برای کاهش فشار انتقال و ارتفاع محل ذخیره سازی، برای بهبود راندمان استخراج داده ها، و به اين ترتيب ظرفیت های تحقق هوشمند داده های بزرگ 130 سانتی متری است. مقاله پیش رو یک مطالعه جامع در مورد تکنیک های فشرده سازی داده های بزرگ هوشمند را ارائه می دهد. توسعه شبکه های هوشمند و خصوصیات و چالش های کاربرد داده های بزرگ الکتریکی ابتدا معرفی شده و سپس تجزیه و تحلیل ویژگی ها و مزایای داده های بزرگ مقیاس بزرگ انجام می پذیرد. در نهایت، این مطالعه بر روی روش های فشرده سازی اطلاعات بالقوه برای داده های بزرگ هوشمند تمرکز می کند و روش های ارزیابی فشرده سازی داده های مقیاس هوشمند را مورد بحث قرار می دهد.
کلمات کلیدی: شبکه هوشمند | مقیاس هوشمند | داده های بزرگ انرژی | فشرده سازی داده ها. |
مقاله ترجمه شده |
8 |
Water source heat pump energy demand prognosticate using disparate data-mining based approaches
منبع آب پمپ حرارتی پیش بینی تقاضای انرژی با استفاده از روش های متفاوت بر اساس داده کاوی-2018 This paper examines the data-mining and supervised based machine learning models for predicting 1-
month ahead cooling load demand of an office building, including the primitive intention of
enhancing the forecasting performance and the accuracy. The data-mining and supervised based ma
chine learning models include; regression support vector machine, Gaussian process regression, scaled
conjugate gradient, tree bagger, boosted tree, bagged tree, neural network, multiple linear regression and
bayesian regularization. The external climate data, hours/day in a week, previous week load, previous
day load and previous 24-h average load are applied as input parameters for these models. Whereas, the
output of the models is the electrical power required for water source heat pump. A water source heat
pump located in Beijing, China, is selected for examining 1-month ahead cooling load forecasting, i.e.,
from July 8 to August 7, 2016. In this paper, simulations are classified into three sessions: 7-days, 14-days
and 1-month. The forecast performance is assessed by computing four performance indices such as mean
square error, mean absolute error, root mean square error and mean absolute percentage error. The mean
absolute percentage error for 7-days ahead cooling load prediction of the water source heat pump from
data-mining based models, Gaussian process regression, tree bagger, boosted tree, bagged tree and
multiple linear regression were 0.405%, 3.544%, 1.928%, 1.703% and 13.053% respectively. While, mean
absolute percentage error of 7-days ahead forecasting in case of machine learning based models such as a
regression support vector machine, Bayesian regularization, scaled conjugate gradient and neural
network were 12.761%, 2.314%, 6.314%, 2.592% respectively. The percentage forecasting error index
proved that the results of data-mining based models are more precise and similar to the existing ma
chine learning models. The results also demonstrate that the better performance and efficiency in
foreseeing the abnormal behaviour in forecasting and future cooling load demand in the building
environment.
Keywords: water source heat pump ، energy demand prediction ، Clustering analysis ، Data-mining |
مقاله انگلیسی |
9 |
Short and medium-term forecasting of cooling and heating load demand in building environment with data-mining based approaches
پیش بینی کوتاه مدت و متوسط مدت تقاضای خنک کننده و گرمای بار در محیط ساختمان با رویکرد مبتنی بر داده کاوی-2018 This paper depicted the novel data mining based methods that consist of six models for predicting accu
rate future heating and cooling load demand of water source heat pump, with the objective of enhancing
the prediction accuracy and the management of future load. The proposed model was developed to ease
generalization to other buildings, by making use of readily available measurements of a comparatively
small number of variables related to water source heat pump operation in the building environment. The
six models are - tree bagger, Gaussian process regression, multiple linear regression, bagged tree, boosted
tree and neural network. The input parameter comprised the prescribed period, external climate data
and the diverse load conditions of water source heat pump. The output was electrical power consump
tion of water source heat pump. In this study, simulations were conducted in three sessions - 7-day,
14-day and 1-month from 8th July to 7th August 2016. The forecast precisions of data mining models
were measured by diverse indices. The performance indices which were used in assessing the prediction
performance were - mean absolute error, coefficient of correlation, coefficient of variation, root mean
square error, mean square error and mean absolute percentage error. The mean absolute percentage er
ror results for 7-day future energy demand forecasting from tree bagger, Gaussian process regression,
bagged tree, boosted tree, neural network and multiple linear regression were 3.544%, 0.405%, 1.703%,
1.928%, 2.592% and 13.053%, respectively. Moreover, when the proposed data mining model performance
was compared with the existing studies, the mean absolute percentage error of 2.515% was found out
for the first session, 7-day. The results also showed that the six models were efficient in foreseeing the
abnormal behavior and future cooling and heating load demand in the building environment.
Keywords: Data mining based approaches ، Water source heat pump ، Clustering Analysis ، Load forecasting |
مقاله انگلیسی |
10 |
باز کردن ظرفیت شبکه توزیع از طریق ردهبندی حرارتی همزمان با نفوذ بالای DGS
سال انتشار: 2014 - تعداد صفحات فایل pdf انگلیسی: 11 - تعداد صفحات فایل doc فارسی: 30 بارگذاری بسیار تصادفی در شبکههای توزیعف عال که به وجود آمده است به این معناست که دستگاههای الکتریکی نیاز است تا از داراییهای خودشان تا بیشترین حد خود با استفاده از ابزارهای مدیریت شبکه هوشمند استفاده کنند. ردهبندی حرارتی همزمان (RTTR)، امکانی برای مدیریت شبکه توزیع فعال و کوتاه مدت و حتی همزمان ارائه میکند تا بتواند بدون آسیب نزدیک یک وضعیت سربار (بار اضافی) اجرا گردد. در این برسی یک RTTR بر اساس چارچوب مدیریت شبکه توزیع فعال فرمولبندی شده است که حدهای ظرفیت شبکه را ساعت به ساعت ارائه میکند.
روابط تصادفی دربارهای مستری و خروجی DG با پاسخهای حرارتی کابلهای زیرزمینی و خطوط بر بار و ترانسفورماتورهای توزیع توصیف شده است و RTTC هم در تمامی مؤلفههای شبکه توزیع با طرحهای شبیهسازی بکار رفت که شامل سطوح مختلف نفوذ DG میشود. این بررسی پتانسیلی برای یک افزایش بهرهوری DG و افزایش بالقوهای برای تأسیسات DG جدید ایجاد کرد در زمانی که RTTR در سیستمهای مدیریت توزیع گنجانده شده بود. کلمات کلیدی: مدیریت شبکه فعال | کنترل تولید توزیع شده | پیشبینی بار | جریان برق | رده بندی حرارتی همزمان | برآورد وضعیت |
مقاله ترجمه شده |