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A review of forecasting algorithms and energy management strategies for microgrids
2018
Systems Science & Control Engineering
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A number of the centralized MAS (multi-agent system) are implemented based on the short-term forecasting information. ...
Short-term load forecast Short-term load forecasting aims at planning an optimal electricity distribution schedule to satisfy the periodical and seasonal load consumption. ...
doi:10.1080/21642583.2018.1480979
fatcat:mqnto4bnvjcpxlx4zjgf4lwt3i
Improved Short Term Energy Load Forecasting Using Web-Based Social Networks
2015
Social Networking
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short term load forecasting process in a smart grid. ...
In this article, we are initiating the hypothesis that improvements in short term energy load forecasting may rely on inclusion of data from new information sources generated outside the power grid and ...
Short Term Load Forecasting Using Web-Based Social Network and Event Schedule Information Recent activities and publications show that a great amount of effort is invested in the research of load forecasting ...
doi:10.4236/sn.2015.44014
fatcat:xtyco6dvrnakhckmeddaohb7ia
State-of-the-art forecasting algorithms for microgrids
2017
2017 23rd International Conference on Automation and Computing (ICAC)
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Therefore, studies on accurate forecasting power generation and load demand are worthwhile in order to build a smart energy management system. ...
Distributed energy sources employ nonpolluted and sustainable resources such as wind and solar power in accordance with local terrain and climate to provide a reliable, consistent power supply for local ...
ACKNOWLEDGEMENT The authors are grateful to Lancaster University's Facility office for providing campus wind turbine operation data and Lancaster Environment Centre for providing weather data. ...
doi:10.23919/iconac.2017.8082049
dblp:conf/iconac/MaM17
fatcat:fjafzquhyjfg3fz3akt4fkhxqu
Computational Intelligence on Short-Term Load Forecasting: A Methodological Overview
2019
Energies
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This work introduces several scientific, technical rationales behind short-term load forecasting methodologies based on works of previous researchers in the energy field. ...
Various computational intelligence techniques and methodologies have been employed in the electricity market for short-term load forecasting, although scant evidence is available about the feasibility ...
[21] proposed a similar-day selection method based on the weather similarity of the forecast day. ...
doi:10.3390/en12030393
fatcat:vcxlu2feubccdjgnvov5kxvehu
Short Term Load Forecasting System Based on Support Vector Kernel Methods
2014
International Journal of Computer Science & Information Technology (IJCSIT)
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The important factors for forecasting involve short, medium and long term forecasting. ...
Factors in short term forecasting comprises of whether data, customer classes, working, non-working days and special event data, while long term forecasting involves historical data, population growth, ...
However, for the short term forecasting, the univariate methods are considered as sufficient as the weather variable smoothly changes in short time frames. ...
doi:10.5121/ijcsit.2014.6308
fatcat:gssu32j6dvg6pmn67d4ihqkoge
Application of bidirectional recurrent neural network combined with deep belief network in short-term load forecasting
2019
IEEE Access
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A hybrid neural network forecasting model based on Deep Belief Network (DBN) and Bidirectional Recurrent Neural Network (Bi-RNN) is proposed. ...
INDEX TERMS Short-term power load forecasting, ensemble empirical mode decomposition, deep belief network, recurrent neural network. 160660 This work is licensed under a Creative Commons Attribution 4.0 ...
In [7] , a short-term load smart grid demand forecasting method based on SVR was proposed. And experimental results demonstrated that the accuracy of load forecasting was acceptable. ...
doi:10.1109/access.2019.2950957
fatcat:lzidp57kb5axhm3hploxm6zggi
Weather satellites and the economic value of forecasts: evidence from the electric power industry
2004
Acta Astronautica
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Satellite data are used to derive improved forecasts for short-term routine weather, long-term climate change, and for predicting natural disasters. ...
economic impacts on electric power distribution. ...
Analysts use different methods to calculate the distribution of historical temperature information and do not agree on a single method for the task. ...
doi:10.1016/j.actaastro.2004.05.015
fatcat:ncqjvewubfge5huy3a7pwfzn7y
A framework for short-term activity-aware load forecasting
2013
Joint Proceedings of the Workshop on AI Problems and Approaches for Intelligent Environments and Workshop on Semantic Cities - AIIP '13
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In this paper, we present a framework for implementing short-term load forecasting, in which statistical time series prediction methods and machine learning-based regression methods, can be configured ...
The first one is to introduce a human activity variable as an additional load influencing factor which reflects anomalous load patterns by aperiodic human activity. ...
Many operational decisions such as generation scheduling, load management and system security assessment are based on short-term forecasts. ...
doi:10.1145/2516911.2516919
dblp:conf/ijcai/DingNSB13
fatcat:kptblxsvljh55h6sbxo4ecvhxu
Short-Term Load Forecast in Microgrids using Artificial Neural Networks
신경회로망을 이용한 마이크로그리드 단기 전력부하 예측
2017
The Transactions of The Korean Institute of Electrical Engineers
신경회로망을 이용한 마이크로그리드 단기 전력부하 예측
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This paper presents an artificial neural network (ANN) based model with a back-propagation algorithm for short-term load forecasting in microgrid power systems. ...
Accurate forecasting in a microgrid will depend on the variables employed and the way they are presented to the ANN. ...
Short-term load forecasting (STLF) deals with load forecasting from one hour up to one week ahead. ...
doi:10.5370/kiee.2017.66.4.621
fatcat:77ohmuxdn5fifox2tcw7k7wsfm
Classification of load forecasting studies by forecasting problem to select load forecasting techniques and methodologies
[article]
2020
arXiv
pre-print
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First, a set of load forecasting studies was built based on relevant load forecasting reviews and forecasting competitions. ...
Then, a second level composed of four Tables summarizes key information about the forecasting tools and the results of these studies. ...
The forecasting horizon is decomposed in a short and long terms. The short-term model is based on half hourly demand and weather information. A model is fitted for each half-hourly period. ...
arXiv:1901.05052v2
fatcat:xnc2spbbfzci3nxnixylmucw2i
Short-Term Load Forecasting Using AMI Data
[article]
2022
arXiv
pre-print
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2020 pre-print arXiv:1912.12479v4 fatcat:wfr57lmgd5bf3anri2fj5m4giuOther Versions
This paper proposes a method called Forecasting using Matrix Factorization (fmf) for short-term load forecasting (stlf). fmf only utilizes historical data from consumers' smart meters to forecast future ...
We empirically evaluate fmf on three benchmark datasets and demonstrate that it significantly outperforms the state-of-the-art methods in terms of load forecasting. ...
Each coordinate (columns) of H is a feature of hours, based on the overall consumption patterns and weather information in A. ...
arXiv:1912.12479v5
fatcat:cohtw4ig6vcxrmuhc2scy5ncna
Environmental and Societal Impacts Group
1999
Disaster Prevention and Management
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short-term weather forecasts. ...
short-term", "local" and "severe", etc. in quantitative terms so that we all understand the needs of the different users of weather information. ...
Participants each fill in a provided matrix of decisionvs-weather information to prepare for breakout groups. ...
doi:10.1108/dpm.1999.07308bag.016
fatcat:h45erxn2lrekxlyvs5hyf2vr5i
Short-Term Load Forecasting Based on Adabelief Optimized Temporal Convolutional Network and Gated Recurrent Unit Hybrid Neural Network
2021
IEEE Access
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[4] proposed a blind Kalman filter method for short-term load forecasting. ...
The short-term load forecasting framework based on the TCN-GRU model is illustrated in Figure 5 . ...
His research interests include machine learning and its application in smart grid, especially load forecasting.
LEI WANG ...
doi:10.1109/access.2021.3076313
fatcat:2y2zmrqcnrhfjidr6p547wnvse
Exogenous Data for Load Forecasting: A Review
[chapter]
2020
Zenodo
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Electrical power load forecasting defines strategies for utilities, power producers and individuals that participate in a smart grid. ...
This review shows the benefit of exogenous data usa ge and the necessity of detailed research on the input features and their influence on detailed, individual level, forecasts of power demand. ...
They expect to know the future demand for maintaining short-term grid balances and for planning grid extensions based on long-term power demand. ...
doi:10.5281/zenodo.4336942
fatcat:qwkzqve5dbcaxiaqhfkxreralq
A Comprehensive Review of the Load Forecasting Techniques Using Single and Hybrid Predictive Models
2020
IEEE Access
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[41] proposes a novel short-term load forecasting method which is based on empirical mode decomposition (EMD) and ELM. ...
FIGURE 4 . 4 Flowchart of simple short-term load forecasting method, showing the modeling and extrapolating process driven by weather, load data and weather forecast values for load prediction. ...
doi:10.1109/access.2020.3010702
fatcat:doeqmfo7eveijlfdvzdyzwgweq
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