Techniques of applying wavelet de-noising into a combined model for short-term load forecasting

Self Cite

Accurate short-term electrical load forecasting plays a pivotal role in the national economy and people's livelihood through providing effective future plans and ensuring a reliable supply of sustainable electricity. Although considerable work has been done to select suitable models and optimize the model parameters to forecast the short-term electrical load, few models are built based on the characteristics of time series, which will have a great impact on the forecasting accuracy. For that reason, this paper proposes a hybrid model based on data decomposition considering periodicity, trend and randomness of the original electrical load time series data. Through preprocessing and analyzing the original time series, the generalized regression neural network optimized by genetic algorithm is used to forecast the short-term electrical load. The experimental results demonstrate that the proposed hybrid model can not only achieve a good fitting ability, but it can also approximate the actual values when dealing with non-linear time series data with periodicity, trend and randomness.

Section: (3) Summation Layermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

Research and Application of a Hybrid Forecasting Model Based on Data Decomposition for Electrical Load Forecasting

Dong

et al. 2016

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“…Unfortunately, affected by several exogenous factors, such as policy, economic production, industrial activities, weather conditions, population, holidays, etc. [3], the electric load data demonstrate seasonality, non-linearity, volatility, randomness and chaos in nature, which increase the difficulty for electric demand forecasting [4].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Chaotic Quantum Bat Algorithm with SVR in Electric Load Forecasting

Geng

Wang

et al. 2017

Abstract:Hybridizing evolutionary algorithms with a support vector regression (SVR) model to conduct the electric load forecasting has demonstrated the superiorities in forecasting accuracy improvements. The recently proposed bat algorithm (BA), compared with classical GA and PSO algorithm, has greater potential in forecasting accuracy improvements. However, the original BA still suffers from the embedded drawbacks, including trapping in local optima and premature convergence. Hence, to continue exploring possible improvements of the original BA and to receive more appropriate parameters of an SVR model, this paper applies quantum computing mechanism to empower each bat to possess quantum behavior, then, employs the chaotic mapping function to execute the global chaotic disturbance process, to enlarge bat's search space and to make the bat jump out from the local optima when population is over accumulation. This paper presents a novel load forecasting approach, namely SVRCQBA model, by hybridizing the SVR model with the quantum computing mechanism, chaotic mapping function, and BA, to receive higher forecasting accuracy. The numerical results demonstrate that the proposed SVRCQBA model is superior to other alternative models in terms of forecasting accuracy.

“…Moreover, many exogenous factors interact with each other, affecting forecasting, such as economic activities, weather conditions, population, industrial production, and others. These effects increase the difficulty of load forecasting [5].…”

Section: Introductionmentioning

confidence: 99%

Application of Hybrid Quantum Tabu Search with Support Vector Regression (SVR) for Load Forecasting

Lee

Lin

2016

Abstract:Hybridizing chaotic evolutionary algorithms with support vector regression (SVR) to improve forecasting accuracy is a hot topic in electricity load forecasting. Trapping at local optima and premature convergence are critical shortcomings of the tabu search (TS) algorithm. This paper investigates potential improvements of the TS algorithm by applying quantum computing mechanics to enhance the search information sharing mechanism (tabu memory) to improve the forecasting accuracy. This article presents an SVR-based load forecasting model that integrates quantum behaviors and the TS algorithm with the support vector regression model (namely SVRQTS) to obtain a more satisfactory forecasting accuracy. Numerical examples demonstrate that the proposed model outperforms the alternatives.