An incremental electric load forecasting model based on support vector regression

Yang, Youlong; Che, Jinxing; Li, YanYing; Zhao, Yu‐Qing; Zhu, Suling

doi:10.1016/j.energy.2016.07.092

Cited by 71 publications

(25 citation statements)

References 43 publications

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“…It is shown that the proposed approach achieves similar results when the number of RBF kernels is less than 60, and after that, the results become worse with the increase in the number of RBF kernels. e ideal value for such parameter is in the range of [5,50]. e iteration number and search agent number (population size) are two key parameters for GWO, whose impacts are shown in Figures 13 and 14, respectively.…”

Section: E Influence Of Gwo-mkelm's Parameter Settingsmentioning

confidence: 99%

“…erefore, they have become more and more popular in energy forecasting. Typical AI models include support-vector regression (SVR) and its extension least-squares SVR (LSSVR) [5,6], artificial neural network (ANN) [7][8][9], extreme learning machine (ELM) [10], sparse Bayesian learning (SBL) [11,12], deep learning [13] (stacked denoising autoencoders (SDAs) [14], deep belief network (DBN) [15], convolutional neural network (CNN) [16], and long short-term memory (LSTM)) [17]), and nature-inspired optimization algorithms [18,19]. For example, Chen et al put forward a new SVR model that used the temperature before demand response as additional input variables for STLF [6].…”

Section: Introductionmentioning

confidence: 99%

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Short-Term Load Forecasting with Improved CEEMDAN and GWO-Based Multiple Kernel ELM

Qian

He³

2020

Complexity

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Short-term load forecasting (STLF) is an essential and challenging task for power- or energy-providing companies. Recent research has demonstrated that a framework called “decomposition and ensemble” is very powerful for energy forecasting. To improve the effectiveness of STLF, this paper proposes a novel approach integrating the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), grey wolf optimization (GWO), and multiple kernel extreme learning machine (MKELM), namely, ICEEMDAN-GWO-MKELM, for STLF, following this framework. The proposed ICEEMDAN-GWO-MKELM consists of three stages. First, the complex raw load data are decomposed into a couple of relatively simple components by ICEEMDAN. Second, MKELM is used to forecast each decomposed component individually. Specifically, we use GWO to optimize both the weight and the parameters of every single kernel in extreme learning machine to improve the forecasting ability. Finally, the results of all the components are aggregated as the final forecasting result. The extensive experiments reveal that the ICEEMDAN-GWO-MKELM can outperform several state-of-the-art forecasting approaches in terms of some evaluation criteria, showing that the ICEEMDAN-GWO-MKELM is very effective for STLF.

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Section: E Influence Of Gwo-mkelm's Parameter Settingsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Short-Term Load Forecasting with Improved CEEMDAN and GWO-Based Multiple Kernel ELM

Qian

He³

2020

Complexity

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“…There are some typical studies: Zhang et al [36] successfully obtained promising results of wind speed forecasting by developing a combined model that consisted of CEEMDAN, five neural networks, CLSFPA, and no negative constraint theory (NNCT). In addition, Che et al [37] developed a kernel-based SVR combination model in a study on electric load prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

Research and Application of a Novel Combined Model Based on Multiobjective Optimization for Multistep-Ahead Electric Load Forecasting

Zhang

Wang

2019

Energies

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Accurate forecasting of electric loads has a great impact on actual power generation, power distribution, and tariff pricing. Therefore, in recent years, scholars all over the world have been proposing more forecasting models aimed at improving forecasting performance; however, many of them are conventional forecasting models which do not take the limitations of individual predicting models or data preprocessing into account, leading to poor forecasting accuracy. In this study, to overcome these drawbacks, a novel model combining a data preprocessing technique, forecasting algorithms and an advanced optimization algorithm is developed. Thirty-minute electrical load data from power stations in New South Wales and Queensland, Australia, are used as the testing data to estimate our proposed model’s effectiveness. From experimental results, our proposed combined model shows absolute superiority in both forecasting accuracy and forecasting stability compared with other conventional forecasting models.

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“…where N = 24 is the forecast horizon; y i andŷ i indicate the actual and the forecast values of hour i. In addition, in Table 2, comparisons are also given by using other prediction methods, namely artificial neural network (ANN) [40], support vector regression (SVR) [41], M5 model tree [31] and multiple linear regression (MLR) [42]. Table 2 shows that RF outperforms all other methods for the STLF.…”

Section: Load Prediction Benchmarkingmentioning

confidence: 99%

A MBCRF Algorithm Based on Ensemble Learning for Building Demand Response Considering the Thermal Comfort

Han

Wang

et al. 2018

Energies

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Demand response (DR) has become an effective and critical method for obtaining better savings on energy consumption and cost. Buildings are the potential demand response resource since they contribute nearly 50% of the electricity usage. Currently, more DR applications for buildings were rule-based or utilized a simplified physical model. These methods may not fully embody the interaction among various features in the building. Based on the tree model, this paper presents a novel model based control with a random forest (MBCRF) learning algorithm for the demand response of commercial buildings. The baseline load of demand response and optimal control strategies are solved to respond to the DR request signals during peak load periods. Energy cost saving of the building is achieved and occupant’s thermal comfort is guaranteed simultaneously. A linguistic if-then rules-based optimal feature selection framework is also utilized to redefine the training and test set. Numerical testing results of the Pennsylvania-Jersey-Maryland (PJM) electricity market and Research and Support Facility (RSF) building show that the load forecasting error is as low as 1.28%. The peak load reduction is up to 40 kW, which achieves a 15% curtailment and outperforms rule-based DR by 5.6%.

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An incremental electric load forecasting model based on support vector regression

Cited by 71 publications

References 43 publications

Short-Term Load Forecasting with Improved CEEMDAN and GWO-Based Multiple Kernel ELM

Short-Term Load Forecasting with Improved CEEMDAN and GWO-Based Multiple Kernel ELM

Research and Application of a Novel Combined Model Based on Multiobjective Optimization for Multistep-Ahead Electric Load Forecasting

A MBCRF Algorithm Based on Ensemble Learning for Building Demand Response Considering the Thermal Comfort

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