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A review of forecasting algorithms and energy management strategies for microgrids

Jie Ma, Xiandong Ma
2018 Systems Science & Control Engineering  
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

Mehmed Kantardzic, Haris Gavranovic, Nedim Gavranovic, Izudin Dzafic, Hanqing Hu
2015 Social Networking  
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

Jie Ma, Xiandong Ma
2017 2017 23rd International Conference on Automation and Computing (ICAC)  
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

Seyedeh Fallah, Mehdi Ganjkhani, Shahaboddin Shamshirband, Kwok-wing Chau
2019 Energies  
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

Lal Hussain, Sajjad Nadeem M, Syed Ahsen Ali Shah
2014 International Journal of Computer Science & Information Technology (IJCSIT)  
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

Xianlun Tang, Yuyan Dai, Qing Liu, Xiaoyuan Dang, Jin Xu
2019 IEEE Access  
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

Henry R. Hertzfeld, Ray A. Williamson, Avery Sen
2004 Acta Astronautica  
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

Yong Ding, Martin Alexander Neumann, Per Goncalves Da Silva, Michael Beigl
2013 Joint Proceedings of the Workshop on AI Problems and Approaches for Intelligent Environments and Workshop on Semantic Cities - AIIP '13  
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

Dae-Won Chung, Seung-Hak Yang, Yong-Min You, Keun-Young Yoon
2017 The Transactions of The Korean Institute of Electrical Engineers  
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]

Jonathan Dumas, Bertrand Cornélusse
2020 arXiv   pre-print
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]

Haris Mansoor, Sarwan Ali, Imdadullah Khan, Naveed Arshad, Muhammad Asad Khan, Safiullah Faizullah
2022 arXiv   pre-print
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  
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

Hanhong Shi, Lei Wang, Rafal Scherer, Marcin Wozniak, Pengchao Zhang, Wei Wei
2021 IEEE Access  
[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]

Ramón Christen, Luca Mazzola, Alexander Denzler, Edy Portmann
2020 Zenodo  
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

Abdullah Al Mamun, Md. Sohel, Naeem Mohammad, Md. Samiul Haque Sunny, Debopriya Roy Dipta, Eklas Hossain
2020 IEEE Access  
[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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