Weekly peak load forecasting for fast-developing cities

Elrazaz, Z.; Al-Mohawes, N.

doi:10.1109/ceej.1986.6591943

Cited by 2 publications

(2 citation statements)

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“…ARIMA is used for non-stationary time series, and SARIMA can deal with non-stationary and cyclical time series. [56] developed two models to forecast weekly peak load one year ahead, where MA model for the seasonal-cyclic component is utilized. In [57], ARMA was used for monthly peak load demand forecast by considering the seasonal patterns and load fluctuations.…”

Section: Stochastic Time Series Modelsmentioning

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: Stochastic Time Series Modelsmentioning

confidence: 99%

Electrical peak demand forecasting- A review

Dai¹,

Meng²,

Dai³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…2) Stochastic Time Series Approach: The time series approach is most popular [38]. Due to accuracy of its results.…”

Section: B Modified Forecasting Techniquesmentioning

confidence: 99%

Analysis and comparison of various methods available for load forecasting: An overview

Khatoon

Ibraheem

Singh

et al. 2014

2014 Innovative Applications of Computational Intelligence on Power, Energy and Controls With Their Impact on Humanity (CIPECH)

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Accurate load forecasting is essential for power system planning and operation. As the electricity end uses expand, load forecasting technique becomes more complicated. Depending on the nature of load variations, one particular method may not be suitable in each case. Before choosing a particular method, an overview of different techniques is required. This paper presents the analysis of various techniques for load forecasting. A comparative analysis is presented to understand the performance of each technique. None of them is found to be perfect alone; the trend is towards hybrid models.Keywords-load forecasting, short term load forecast, long term load forecast.I.

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Weekly peak load forecasting for fast-developing cities

Cited by 2 publications

References 0 publications

Electrical peak demand forecasting- A review

Electrical peak demand forecasting- A review

Analysis and comparison of various methods available for load forecasting: An overview

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