Short-term Electric Load Forecasting Based on Wavelet Neural Network, Particle Swarm Optimization and Ensemble Empirical Mode Decomposition

López, Cristian; Zhong, Wei; Zheng, Menglian

doi:10.1016/j.egypro.2017.03.847

Cited by 22 publications

(4 citation statements)

References 14 publications

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“…For users with strong correlation at adjacent moments, local similar data are filtered out with the help of improved Fuzzy C-Mean clustering (FCM), integrating the load value of the adjacent moments into new input features [80] [55]. For users with weak correlation at adjacent moments, the local similar daily data are utilized as features [60] [40].…”

Section: -Fuzzy C-means Clusteringmentioning

confidence: 99%

Customer active power consumption prediction for the next day based on historical profile

Goudah

El-Habrouk

Schramm³

et al. 2022

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Section: -Fuzzy C-means Clusteringmentioning

confidence: 99%

Customer active power consumption prediction for the next day based on historical profile

Goudah

El-Habrouk

Schramm³

et al. 2022

Ad in Comp & ENG

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“…In the past few years, many scholars have given a lot of attention to short-term power load forecasting based on different factors and have reached a higher level. Annual power load forecasting can provide reliable guidance for power grid operation and power construction planning [ 1 – 4 ]. Affected by economic, climate and other factors, an annual load curve often shows a non-linear characteristic and has a certain degree of mutation.…”

Section: Introductionmentioning

confidence: 99%

A VVWBO-BVO-based GM (1,1) and its parameter optimization by GRA-IGSA integration algorithm for annual power load forecasting

Wang

2018

PLoS ONE

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Annual power load forecasting is not only the premise of formulating reasonable macro power planning, but also an important guarantee for the safety and economic operation of power system. In view of the characteristics of annual power load forecasting, the grey model of GM (1,1) are widely applied. Introducing buffer operator into GM (1,1) to pre-process the historical annual power load data is an approach to improve the forecasting accuracy. To solve the problem of nonadjustable action intensity of traditional weakening buffer operator, variable-weight weakening buffer operator (VWWBO) and background value optimization (BVO) are used to dynamically pre-process the historical annual power load data and a VWWBO-BVO-based GM (1,1) is proposed. To find the optimal value of variable-weight buffer coefficient and background value weight generating coefficient of the proposed model, grey relational analysis (GRA) and improved gravitational search algorithm (IGSA) are integrated and a GRA-IGSA integration algorithm is constructed aiming to maximize the grey relativity between simulating value sequence and actual value sequence. By the adjustable action intensity of buffer operator, the proposed model optimized by GRA-IGSA integration algorithm can obtain a better forecasting accuracy which is demonstrated by the case studies and can provide an optimized solution for annual power load forecasting.

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“…In this category, articles are presented like BPNN (Li et al [23]), WTBPNN (Changhao et al [24]), GNBPNN (Irani et al [25]), NNPSO (Zhaoyu et al [26]), WT-ANFIS (Karthika et al [27]), ADE-BPNN (Wang et al [28]), Wavelet-PSO-ANFIS,) Catalao et al [29]), and WT-PSO-BPNN (Mandel et al [30]) whose goal is to arrive at the highest accuracy in forecasting. Some other research are found in [10,[31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50]. Figure 1 shows the development of the new technique.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Forecasting of Electricity Supply and Demand by Using the Wavelet-PSO-NNs-SO Technique for Searching in Big Data of Iran’s Electricity Market

Salami

Sobhani

Ghazizadeh

2018

Data

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The databases of Iran’s electricity market have been storing large sizes of data. Retail buyers and retailers will operate in Iran’s electricity market in the foreseeable future when smart grids are implemented thoroughly across Iran. As a result, there will be very much larger data of the electricity market in the future than ever before. If certain methods are devised to perform quick search in such large sizes of stored data, it will be possible to improve the forecasting accuracy of important variables in Iran’s electricity market. In this paper, available methods were employed to develop a new technique of Wavelet-Neural Networks-Particle Swarm Optimization-Simulation-Optimization (WT-NNPSO-SO) with the purpose of searching in Big Data stored in the electricity market and improving the accuracy of short-term forecasting of electricity supply and demand. The electricity market data exploration approach was based on the simulation-optimization algorithms. It was combined with the Wavelet-Neural Networks-Particle Swarm Optimization (Wavelet-NNPSO) method to improve the forecasting accuracy with the assumption Length of Training Data (LOTD) increased. In comparison with previous techniques, the runtime of the proposed technique was improved in larger sizes of data due to the use of metaheuristic algorithms. The findings were dealt with in the Results section.

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Short-term Electric Load Forecasting Based on Wavelet Neural Network, Particle Swarm Optimization and Ensemble Empirical Mode Decomposition

Cited by 22 publications

References 14 publications

Customer active power consumption prediction for the next day based on historical profile

Customer active power consumption prediction for the next day based on historical profile

A VVWBO-BVO-based GM (1,1) and its parameter optimization by GRA-IGSA integration algorithm for annual power load forecasting

Short-Term Forecasting of Electricity Supply and Demand by Using the Wavelet-PSO-NNs-SO Technique for Searching in Big Data of Iran’s Electricity Market

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