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Short-Term Load Forecasting via Integrated Incremental Extreme Support Vector Regression Approach
2016
Innovative Computing Information and Control Express Letters, Part B: Applications
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In this paper, a novel short-term load forecasting (STLF) model based on integrated incremental extreme support vector regression (II-ESVR) approach is presented. ...
We show attractive experimental results to highlight the system efficiency and stability by using our integrated IESVR approach to forecast short-term power load. ...
Short-Term Load Forecasting Based on Integrated IESVR Method. ...
doi:10.24507/icicelb.07.05.977
fatcat:2uyyfpp43nbetpmw4ij35zhiqq
Incremental Learning Model for Load Forecasting without Training Sample
2022
Computers Materials & Continua
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This article presents hourly load forecasting by using an incremental learning model called Online Sequential Extreme Learning Machine (OS-ELM), which can learn and adapt automatically according to new ...
Both the proposed method and FOS-ELM are used for hourly load forecasting from the Hourly Energy Consumption dataset. ...
Very short-term load forecasting is a forecast up to 1 h in advance where the forecast result is often used to control the power system quality. 2. ...
doi:10.32604/cmc.2022.028416
fatcat:adto26yun5fo3k2yngot42ligm
Short-Term Load Forecasting Based on the Analysis of User Electricity Behavior
2016
Algorithms
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Finally, the load forecasting model based on the Online Sequential Extreme Learning Machine (OS-ELM) is applied to different clusters to conduct load forecasting and the load forecast is summed to obtain ...
of load forecasting. ...
on the forecasting accuracy. { ( , ) | , , 1, ......, } n n i i i i z x t x R t R i N = ∈ ∈ = ,
Short-Term Load Forecasting Model Based on OS-ELM
OS-ELM Algorithm OS-ELM (Online Sequential Extreme ...
doi:10.3390/a9040080
fatcat:73jddszdjjcuhhznpkkiwvhxrq
Agriculture Customers Power Consumption Analysis to Reduced Power Losses in Winter
2019
International Journal of Engineering and Advanced Technology
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In view of this, most Electricity Boards supply power to agriculture sector and claim subsidy from the State Govt. based on energy consumption. ...
To increase the food output, almost all the State Governments show benevolence to farmers and arrange supply of electric power for irrigation to the farmers at a nominal rate, and in some States, without ...
LOAD FORECASTING load forecasting can be categorized into Short-Term Load Forecasting, Medium-Term Load Forecasting, and Long-Term Load Forecasting. ...
doi:10.35940/ijeat.a9596.109119
fatcat:fnicr2sbk5ffrjhbm2ckv6lvw4
Improved Neural Networks with Random Weights for Short-Term Load Forecasting
2015
PLoS ONE
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This paper proposes a new forecasting model based on the improved neural networks with random weights (INNRW). ...
An effective forecasting model for short-term load plays a significant role in promoting the management efficiency of an electric power system. ...
Load forecasting can be classified into long-term, mid-term, short-term and very shortterm forecasting, based on the forecasting horizon. ...
doi:10.1371/journal.pone.0143175
pmid:26629825
pmcid:PMC4667993
fatcat:2jbvcav6mfd2rhovfta46ruram
Cloud Computing and Extreme Learning Machine for a Distributed Energy Consumption Forecasting in Equipment-Manufacturing Enterprises
2016
Cybernetics and Information Technologies
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Therefore, this paper proposes an intellectualized, short-term distributed energy consumption forecasting model for equipment-manufacturing enterprises based on cloud computing and extreme learning machine ...
Furthermore, the proposed forecasting algorithm possesses excellent parallel performance, overcomes the shortcoming of a single computer's insufficient computing power when facing massive and high-dimensional ...
ELM is introduced into the power load forecasting for the first time in the literature [28] , and the better prediction performance is obtained. ...
doi:10.1515/cait-2016-0079
fatcat:457jzbkwzfflvicxymmffvxs7i
Multi-Horizon Electricity Load and Price Forecasting using an Interpretable Multi-Head Self-Attention and EEMD-Based Framework
2021
IEEE Access
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On the basis of the time period, forecasting can be classified as short, medium and long term. Shortterm load forecasting (STLF) is based upon intraday and day-ahead power system operations. ...
Long-term load forecasting (LTLF) is mainly utilized in power generation and transmission system planning. ...
doi:10.1109/access.2021.3086039
fatcat:bvibebuf3zgtdgxbbruei7bdmq
Artificial Intelligence Techniques in Smart Grid: A Survey
2021
Smart Cities
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This survey presents a structured review of the existing research into some common AI techniques applied to load forecasting, power grid stability assessment, faults detection, and security problems in ...
the smart grid and power systems. ...
Based on the data provided by smart meters, many techniques are proposed and applied for power system LF.
Short-Term Load Forecasting Qiu et al. ...
doi:10.3390/smartcities4020029
doaj:85074e6b64e546c8b5b61351aad66daa
fatcat:2n46ot2yeveuxitvrfce6drne4
Robust Data Predictive Control Framework for Smart Multi-Microgrid Energy Dispatch Considering Electricity Market Uncertainty
2021
IEEE Access
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The OR-ELM regression method shows a significant forecasting performance in terms of error metrics. ...
The proposed framework solves the economic energy dispatch based on an accurate Electricity Price Forecasting (EPF) by an Outlier-Robust Extreme Learning Machine (OR-ELM) algorithm and a two layers cooperative ...
In [31] an Accurate Electricity price Forecasting (EPF) strategy based on long-short term memory (LSTM) for forecasting the electricity markets of Pennsylvania-New Jersey-Maryland. ...
doi:10.1109/access.2021.3060315
fatcat:2howphvyavafbdp2ndqnlmzucu
Very Short-Term Nonparametric Probabilistic Forecasting of Renewable Energy Generation— With Application to Solar Energy
2016
IEEE Transactions on Power Systems
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We rely on an Extreme Learning Machine (ELM) as a fast regression model, trained in varied ways to obtain both point and quantile forecasts of solar power generation. ...
This therefore motivates the proposal of a nonparametric approach to generate very short-term predictive densities, i.e., for lead times between a few minutes to one hour ahead, with fast frequency updates ...
The online short-term PV forecasting using a clear sky model described in [3] comprises a notable example of a relevant approach. Perez et. al. ...
doi:10.1109/tpwrs.2015.2502423
fatcat:jfuq27wgw5ccldsa7uoa56mlde
Non-Gaussian Residual based Short Term Load Forecast Adjustment for Distribution Feeders
2020
IEEE Access
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INDEX TERMS Load modeling, power systems, renewable generation. ...
Improvements in accuracy are demonstrated on benchmark load forecast models at distribution level low voltage substations. ...
Short term visibility of power flows through accurate forecasts are integral to ensuring these objectives. ...
doi:10.1109/access.2020.2965320
fatcat:d2aym5d2rrfnvls2jdwzdgehrq
HSIC Bottleneck based Distributed Deep Learning Model for Load Forecasting in Smart Grid with A Comprehensive Survey
2020
IEEE Access
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Generally, there are four types of load forecasting: long-term load forecasting (LTLF), mid-term or medium-term load forecasting (MTLF), short-term load forecasting (STLF), and very short term load forecasting ...
Each work focuses on one or two long-term, short-term, very short-term, and medium-term demands. How the proposed technique may apply to the other demands is open for investigation. ...
doi:10.1109/access.2020.3040083
fatcat:tsqokovkm5gpfdsnm7bph73piu
Electrical Load Forecasting: A methodological overview
2020
International Journal of Engineering & Technology
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Thus Accurate electric load forecast is needed for power system security and reliability. ...
It also improves energy efficiency, revenues for the electrical companies and reliable operation of a power system.In recent times, there are significant proliferations in the implementation of forecasting ...
Based on the forecasting horizon, the load forecasting process can be classified into four categories: very short term, short term, medium term and long term load forecasting [9] . ...
doi:10.14419/ijet.v9i3.30706
fatcat:yv4ocnzc3jaoblys7fqiekytj4
Short-Term Electric Load and Price Forecasting Using Enhanced Extreme Learning Machine Optimization in Smart Grids
2019
Energies
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On the basis of selected features, classification is performed using ELR. Cross validation is done for ERELM using Monte Carlo and K-Fold methods. ...
The sole purpose of this work is to predict the price and load efficiently. ...
In literature, short term load and price forecasting using the conventional techniques is performed on individual basis mostly, whereas we used short term load and price forecasting simultaneously using ...
doi:10.3390/en12050866
fatcat:bav3gbfic5ccpfeuab36ap7ujq
Advances in the Application of Machine Learning Techniques for Power System Analytics: A Survey
2021
Energies
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of power flows, power quality, photovoltaic systems, intelligent transportation, and load forecasting. ...
Trying to answer these questions, this manuscript presents a systematic review of the state-of-the-art studies implementing ML techniques in the context of power systems, with a specific focus on the analysis ...
Short-and Medium-Term Load
Prediction
Real hourly data of electricity load of ISO New
England (2003-2016)
PCA, LSTM,
XGBoost with
K-means
Table 6 . 6 Cont. ...
doi:10.3390/en14164776
fatcat:cr2j3psazfeztk3qaeu7giw5sa
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