Artificial neural network based electric peak load forecasting

Park, D.C.; Mohammed, Osama A.

doi:10.1109/secon.1991.147742

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…Therefore, more research efforts on the interaction between electricity usage decisions of end users and disaggregated load forecasting are needed in the future. [136], [46], [39], [42], [28], [45], [48], [41], [114], [131], [130], [32], [137], [40], [44], [138], [111], [122], [139], [ [92], [68], [21], [69], [62], [65], [58], [59], [46][60], [66], [ [45], [85], [145], [90], [92], [87], [146], [147], [139], [148], [94], [95], [149], [84], [150], [98], [151], [152], [153], [132],…”

Section: Summary Of the Reviewed Studiesmentioning

confidence: 99%

Electrical peak demand forecasting- A review

Dai¹,

Meng²,

Dai³

et al. 2021

Preprint

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The power system is undergoing rapid evolution with the roll-out of advanced metering infrastructure and local energy applications (e.g. electric vehicles) as well as the increasing penetration of intermittent renewable energy at both transmission and distribution level, which characterizes the peak load demand with stronger randomness and less predictability and therefore poses a threat to the power grid security. Since storing large quantities of electricity to satisfy load demand is neither economically nor environmentally friendly, effective peak demand management strategies and reliable peak load forecast methods become essential for optimizing the power system operations. To this end, this paper provides a timely and comprehensive overview of peak load demand forecast methods in the literature. To our best knowledge, this is the first comprehensive review on such topic. In this paper we first give a precise and unified problem definition of peak load demand forecast. Second, 139 papers on peak load forecast methods were systematically reviewed where methods were classified into different stages based on the timeline. Thirdly, a comparative analysis of peak load forecast methods are summarized and different optimizing methods to improve the forecast performance are discussed. The paper ends with a comprehensive summary of the reviewed papers and a discussion of potential future research directions.

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Section: Summary Of the Reviewed Studiesmentioning

confidence: 99%

Electrical peak demand forecasting- A review

Dai¹,

Meng²,

Dai³

et al. 2021

Preprint

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“…Earlier studies consider historical loading conditions combined with temperature as the predictors of hourly or daily load [4]- [8], with several studies referencing that utilizing wind speed and humidity might lead to higher accuracies [6], [9]. Reference [10] even attempted to differentiate between weekdays, weekends, and holidays its training data.…”

Section: Previous Load Forecasting Studiesmentioning

confidence: 99%

Next-Hour Electrical Load Forecasting using an Artificial Neural Network: Applicability in the Philippines

Torrizo¹,

Africa²

2019

IJATCSE

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Load forecasting remains as an important activity for the power systems industry, being a critical ystem. With the increasing amount of implemented microgrids across the Philippines, there is merit in investigating localized load forecasting schemes for use in assigning the dispatch of the microgrid's resource at any given time. This study reviews the use of an artificial neural network (ANN) as a next-hour load forecasting method in the Philippine setting.

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“…However, there are drawbacks for the complex algorithmic methods based on AI technique. For instance, they may converge slowly and even diverge in certain cases [5]. Their relatively high requirement of time and space confines their response speed to the latest information from measurements.…”

Section: Different Approaches For Load Forecastingmentioning

confidence: 99%

An Innovative Application Method of Monthly Load Forecasting for Smart IEDs

Choi¹,

Liang²,

Lee³

et al. 2013

Journal of Electrical Engineering and Technology

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-This paper develops a new Intelligent Electronic Device (IED), and then presents an application method of a monthly load forecasting algorithm on the smart IEDs. A Multiple Linear Regression (MLR) model implemented with Recursive Least Square (RLS) estimation is established in the algorithm. Case Study proves the accuracy and reliability of this algorithm and demonstrates the practical meanings through designed screens. The application method shows the general way to make use of IED's smart characteristics and thereby reveals a broad prospect of smart function realization in application.

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Artificial neural network based electric peak load forecasting

Cited by 6 publications

References 13 publications

Electrical peak demand forecasting- A review

Electrical peak demand forecasting- A review

Next-Hour Electrical Load Forecasting using an Artificial Neural Network: Applicability in the Philippines

An Innovative Application Method of Monthly Load Forecasting for Smart IEDs

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