Load current forecasting using statistical analysis

Nagumo, T.; Ito, Hirokazu; Sano, Tsuneyo

doi:10.1049/oap-cired.2017.0304

Cited by 5 publications

(2 citation statements)

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“…Thus, a persistence technique for both renewable energy and load forecasting is presented in [44] which is based on historical power data instead of weather data. Other techniques presented in the literature for load and generation forecasting include fuzzy logic [45], [46], statistic approach [47], [48], intelligent algorithm [49], adaptive neuro-fuzzy inference system (ANFIS) [50]. However, further accurate load and generation forecasting for DC microgrids can be the future research directions combining those methods or models as a hybrid one.…”

Section: A Generation and Load Forecastingmentioning

confidence: 99%

DC Microgrid Planning, Operation, and Control: A Comprehensive Review

Al–Ismail

2021

IEEE Access

293

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In recent years, due to the wide utilization of direct current (DC) power sources, such as solar photovoltaic (PV), fuel cells, different DC loads, high-level integration of different energy storage systems such as batteries, supercapacitors, DC microgrids have been gaining more importance. Furthermore, unlike conventional AC systems, DC microgrids do not have issues such as synchronization, harmonics, reactive power control, and frequency control. However, the incorporation of different distributed generators, such as PV, wind, fuel cell, loads, and energy storage devices in the common DC bus complicates the control of DC bus voltage as well as the power-sharing. In order to ensure the secure and safe operation of DC microgrids, different control techniques, such as centralized, decentralized, distributed, multilevel, and hierarchical control, are presented. The optimal planning of DC microgrids has an impact on operation and control algorithms; thus, coordination among them is required. A detailed review of the planning, operation, and control of DC microgrids is missing in the existing literature. Thus, this article documents developments in the planning, operation, and control of DC microgrids covered in research in the past 15 years. DC microgrid planning, operation, and control challenges and opportunities are discussed. Different planning, control, and operation methods are well documented with their advantages and disadvantages to provide an excellent foundation for industry personnel and researchers. Power-sharing and energy management operation, control, and planning issues are summarized for both grid-connected and islanded DC microgrids. Also, key research areas in DC microgrid planning, operation, and control are identified to adopt cuttingedge technologies. This review explicitly helps readers understand existing developments on DC microgrid planning, operation, and control as well as identify the need for additional research in order to further contribute to the topic. INDEX TERMS DC microgrids, renewable energy sources, batteries, supercapacitors, DC bus voltage, power management, state of charge, microgrid operation, and planning.

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Section: A Generation and Load Forecastingmentioning

confidence: 99%

DC Microgrid Planning, Operation, and Control: A Comprehensive Review

Al–Ismail

2021

IEEE Access

293

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“…Two other methodologies were used: one predicts loads individually and another uses the participation factors and global load forecasting. For example, in [28], the authors propose a methodology for predicting the electrical current in several transmission lines based on a statistical method. Reference [29] presents a system called MOSAIC that uses a bottom-up simulation tool to determine the current and future consumption and production load curve in an area of the electrical system.…”

Section: Related Workmentioning

confidence: 99%

Multinodal load forecasting for distribution systems using a fuzzy-artmap neural network

Abreu

Amorim

Santos-Junior

et al. 2018

Applied Soft Computing

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This work proposes a predictor system (multinodal forecasting) considering several points of an electrical network, such as substations, transformers, and feeders, based on an adaptive resonance theory (ART) neural network family. It is a problem similar to global forecasting, with the main difference being the strategy to align the input and output of the data with several parallel neural modules. Considering that multinodal prediction is more complex compared to global prediction, the multinodal prediction will use a fuzzy-ARTMAP neural network and a global load participation factor. The advantages of this approach are as follows: (1) the processing time is equivalent to the processing required for global forecasting (i.e., the additional time processing is quite low); and (2) Fuzzy-ARTMAP neural networks converge significantly faster than backpropagation neural networks (improved benchmark in precision). The preference for neural networks of the ART family is due to the characteristic stability and plasticity that these architectures have to provide results in a fast and precise way. To test the proposed forecast system, the results are presented for nine substations from the database of an electrical company.

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Regression Analysis-Based Load Modelling for Electric Distribution Networks

Grigoraș

Neagu

2021

Power Systems

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Load current forecasting using statistical analysis

Cited by 5 publications

References 0 publications

DC Microgrid Planning, Operation, and Control: A Comprehensive Review

DC Microgrid Planning, Operation, and Control: A Comprehensive Review

Multinodal load forecasting for distribution systems using a fuzzy-artmap neural network

Regression Analysis-Based Load Modelling for Electric Distribution Networks

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