An annual load forecasting model based on support vector regression with differential evolution algorithm

Wang, Jianjun; Li, Li; Niu, Dongxiao; Tan, Zhongfu

doi:10.1016/j.apenergy.2012.01.010

Cited by 226 publications

(111 citation statements)

References 25 publications

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“…Linear regression model proves to be very useful for predicting short-term peak loads [10,29] and annual loads [43]. The later research uses support vector regression and various functional linear regression models for the actual prediction.…”

Section: Regression Modelsmentioning

confidence: 99%

“…Research on forecasting annual loads in combination with differential evaluation and support vector regression was reported in [43]. The authors of [43] resorted to differential evaluations so as that to choose parameters from an available predefined set of parameters while the regression is used for the actual forecasting.…”

Section: Regression Modelsmentioning

confidence: 99%

“…The authors of [43] resorted to differential evaluations so as that to choose parameters from an available predefined set of parameters while the regression is used for the actual forecasting.…”

Section: Regression Modelsmentioning

confidence: 99%

See 2 more Smart Citations

A Pattern Recognition Approach for Peak Prediction of Electrical Consumption

Goodwin

Yazidi

2014

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Abstract.Demand peaks in electrical power consumptions pose serious challenges for energy companies as these are typically unforeseen and require the net to support abnormally high consumption levels. Such peaks can be regulated in smart energy grids with the introduction of relatively simple techniques such as load balancing and smart pricing strategies. This is, however, difficult in practice because it requires prediction of peaks prior to their actual occurrence.While most studies formulate the problem as an estimation problem, we take a radically different approach and formulate it as a classical pattern recognition problem. Further, the paper applies classification methods to solve the problem and applies these with real-life data from a Norwegian smart grid pilot project. Some of the key findings are that the algorithms can accurately detect 80% of energy consumption peaks up to one week ahead of time. Furthermore, we introduce a novel Learning Automata based approaches for selecting the optimal prediction model from a pool of models in an online fashion.

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Section: Regression Modelsmentioning

confidence: 99%

Section: Regression Modelsmentioning

confidence: 99%

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A Pattern Recognition Approach for Peak Prediction of Electrical Consumption

Goodwin

Yazidi

2014

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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“…Support vector regression (SVR) is the most common application form of support vector machine (SVM). An overview of the basic ideas underlying SVM for regression and function estimation has been given in [48], and its use in load forecasting is reported in literatures [49][50][51][52][53].…”

Section: Definition References For Mtlf and Ltlf Are Elaborated In Dmentioning

confidence: 99%

Forecasting the load of electrical power systems in mid‐ and long‐term horizons: a review

Khuntia

Rueda

Meijden

2016

IET Generation, Transmission & Distribution

134

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Load forecasting has always been an important part in the planning and operation of electric utilities, i.e., both transmission and distribution companies. With technological advancement, change in economic condition and many other factors (to be discussed in this work), load forecasting is becoming more important. The forecast affects as well as gets affected because of the load impacting factors and actions taken in different time horizons. However, due to its stochastic and uncertainty characteristics, it has been one challenging problem for electrical utilities to accurately forecast future load demand. This paper aims at reviewing the different load forecasting techniques developed for the mid-and long-term horizons of electrical power systems. Since there has never been an explicit literature study of the various forecasting techniques for mid-and long-term horizons, this paper reviews techniques for each of the forecasting horizons, citing various methodologies developed so far supported by published literature. The study is concluded with discussion on future research directions.

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“…However, forecasting the electrical load is very difficult as electrical loads are frequently affected by several factors such as irregular behaviours, social, time and other non-linear factors [1].…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Performance of Building Load Forecasting Using Hybrid of GLSSVM – ABC Model

et al. 2016

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Abstract. In conducting load forecasting, the accuracy of forecasting is an important aspect in planning and managing electricity. Thus, a new hybrid model is presented in this paper, which combines the Group Method of Data Handling, Least Square Support Vector Machine and Artificial Bee Colony (GLSSVM-ABC) for building load forecasting. Its performance accuracy has been compared with other methods by using the Mean Absolute Percentage Error (MAPE) and Root Means Square Error (RMSE). It was found that the proposed method has resulted in better performance accuracy in terms of both MAPE and RMSE. The MAPE analysis showed an increase in performance accuracy of more than 7 percent when compared to other methods. The RMSE analysis showed an increase in performance accuracy of more than 5 percent when compared to other methods. The results in this study showed that the proposed method is proven to be effective and has great potential for accurate building load forecasting.

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An annual load forecasting model based on support vector regression with differential evolution algorithm

Cited by 226 publications

References 25 publications

A Pattern Recognition Approach for Peak Prediction of Electrical Consumption

A Pattern Recognition Approach for Peak Prediction of Electrical Consumption

Forecasting the load of electrical power systems in mid‐ and long‐term horizons: a review

Enhancing the Performance of Building Load Forecasting Using Hybrid of GLSSVM – ABC Model

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