The maximum power demand forecasting with fuzzy theory

Lee, Ming-Rong; Wang, Shun-Jih; YiYu, Lu; Tai, Liang-I; Shi, Huancong

doi:10.1109/3ca.2010.5533319

Cited by 3 publications

(2 citation statements)

References 2 publications

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“…The experiment demonstrated that the proposed hybrid model had good performance on both short and mid-term load demand forecast. FL + ES [77][80] FL + SOM [27] FL + GA [118] ANNs [90] ANN + SOM [119] ANN + SOM + PCA [96] ANN + GA [17][53] [120] GA + RBFN + SVM [33]…”

Section: Hybrid Techniquesmentioning

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: Hybrid Techniquesmentioning

confidence: 99%

Electrical peak demand forecasting- A review

Dai¹,

Meng²,

Dai³

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…There are numerous power demand forecasting theories and applications currently [6] [7] [8] [9] [10]. However, they simply focus on either macroscopically long-term impacts or probabilistic factors without integration of these two kinds of variables.…”

Section: Introductionmentioning

confidence: 99%

Hybrid forecasting model of power demand based on three-stage synthesis and stochastically self-adapting mechanism

Dang

Baker

et al. 2014

2014 IEEE International Energy Conference (ENERGYCON)

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The power demand over the electrical power system and smart grid is a random function in the time domain which is affected by a larger number of stochastic factors, for example weather, date and economy as well as a series of unpredictable human factors. Therefore, the most convenient and efficient methodology to forecast the power demand is a stochastic model based on statistics and fuzzy mathematics, because it can merge all complex factors which are difficult or even impossible to be modelled mathematically into an appropriate correction variable. In this paper, we will introduce a hybrid forecasting model of power demand which separates the forecasting process into three stages, i.e. long-term, middle-term and short-term analysis. Most of the long-term factors will be combined in a comprehensive correction factor for the middle-term stage. In the middle-term stage the forecasting mechanism integrates several different forecasting principles and methods to produce a combined forecasting result and dynamically adjusts its forecasting scheme by different weights for different forecasting methods by measuring and comparing the forecasting result and its corresponding practical measurement. By this self-adapting algorithm, the forecasting model is able to forecast the next 24hour power demand via using the historical data obtained in its database. In the short-term stage, a fine adjustment mechanism will be involved to enhance the reliability and robustness of the holistic forecasting mechanism.

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An enhanced stochastic algorithm for optimal contract capacity of furniture stores

Tsai

Huang

et al. 2020

IOP Conf. Ser.: Mater. Sci. Eng.

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This paper integrated the Grey Theory and Particle Swarm Optimization to propose an Enhanced Stochastic Algorithm (ESA) for dealing with the optimal contact capacity of the furniture store. ESA is used to find the minimal total annual electricity bill and simultaneously find the optimal contract capacity under the operational and system’s constraints. The historical demand for a furniture store is first collected in this paper. Grey theory is used to predict the next year demand of every month. Based on the Time-Of-Use (TOU) rates employed by the Taiwan Power Company (TPC), the annual electric bill including basic charge and penalty charge is derived to formulate an objective function. Particle Swarm Optimization (PSO) was adopted to minimize the annual electricity bill and simultaneously find the optimal contract capacity. Practical load consumption data were used to prove the validity of the ESA. Users can able to estimate the contract capacity by themselves and curtail the electricity cost.

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The maximum power demand forecasting with fuzzy theory

Cited by 3 publications

References 2 publications

Electrical peak demand forecasting- A review

Electrical peak demand forecasting- A review

Hybrid forecasting model of power demand based on three-stage synthesis and stochastically self-adapting mechanism

An enhanced stochastic algorithm for optimal contract capacity of furniture stores

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