Long Term Load Forecasting and Recommendations for China Based on Support Vector Regression

Ye, Shijie; Zhu, Guangfu; Xiao, Zhi

doi:10.4236/epe.2012.45050

Cited by 12 publications

(7 citation statements)

References 16 publications

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“…It becomes popular in peak summer or winter. For load duration from few weeks to many years, LTLF is considered [40]. The factors including weather data, characteristics of install devices at areas of interest, history of load and numbers of customers are accounted in it.…”

Section: Figure 3 Number Of Publications With Respect To Time Scalementioning

confidence: 99%

Load Forecasting Techniques for Power System: Research Challenges and Survey

et al. 2022

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The main and pivot part of electric companies is the load forecasting. Decision-makers and think tank of power sectors should forecast the future need of electricity with large accuracy and small error to give uninterrupted and free of load shedding power to consumers. The demand of electricity can be forecasted amicably by many Machine Learning (ML), Deep Learning (DL) and Artificial Intelligence (AI) techniques among which hybrid methods are most popular. The present technologies of load forecasting and present work regarding combination of various ML, DL and AI algorithms are reviewed in this paper. The comprehensive review of single and hybrid forecasting models with functions; advantages and disadvantages are discussed in this paper. The comparison between the performance of the models in terms of Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) values are compared and discussed with literature of different models to support the researchers to select the best model for load prediction. This comparison validates the fact that the hybrid forecasting models will provide a more optimal solution.INDEX TERMS load forecasting, machine learning, load shedding, root mean squared error, mean absolute percentage error.

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Section: Figure 3 Number Of Publications With Respect To Time Scalementioning

confidence: 99%

Load Forecasting Techniques for Power System: Research Challenges and Survey

et al. 2022

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“…It is popular for forecasting load in seasonal changes such as winter or peak-summer etc. LTLF is used for lead time from few weeks to several years [16]. It takes into account the historical load and weather data, customer's number in categories, the characteristics of the appliances of the area etc.…”

Section: Categories Of Load Forecastingmentioning

confidence: 99%

A Comprehensive Review of the Load Forecasting Techniques Using Single and Hybrid Predictive Models

et al. 2020

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Load forecasting is a pivotal part of the power utility companies. To provide load-shedding free and uninterrupted power to the consumer, decision-makers in the utility sector must forecast the future demand for electricity with a minimum amount of error percentage. Load prediction with less percentage of error can save millions of dollars to the utility companies. There are numerous Machine Learning (ML) techniques to amicably forecast the demand of electricity among which the hybrid models show the best result. Two or more than two predictive models are amalgamated to design a hybrid model, each of which provides improved performances by the merit of individual algorithms. This paper reviews the current stateof-the-art of electric load forecasting technologies and presents recent works pertaining to the combination of different ML algorithms into two or more methods for the construction of hybrid models. A comprehensive study of each single and multiple load forecasting model is performed with an in-depth analysis of their advantages, disadvantages, and functions. A comparison between their performance in terms of Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) values are developed with pertinent literature of several models to aid the researchers with the selection of suitable models for load prediction.

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“…And it can be described by introducing (nonnegative) slack variables , * to measure the deviation of training samples outside -insensitive zone. Figure 4 shows the data map to the high-dimensional feature space [30,31]. Thus SVR is formulated as minimization of the following function:…”

Section: Support Vector Regression (Svr)mentioning

confidence: 99%

Support Vector Regression and Genetic Algorithm for HVAC Optimal Operation

Chen

Chang

2016

Mathematical Problems in Engineering

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This study covers records of various parameters affecting the power consumption of air-conditioning systems. Using the Support Vector Machine (SVM), the chiller power consumption model, secondary chilled water pump power consumption model, air handling unit fan power consumption model, and air handling unit load model were established. In addition, it was found that 2 of the models all reached 0.998, and the training time was far shorter than that of the neural network. Through genetic programming, a combination of operating parameters with the least power consumption of air conditioning operation was searched. Moreover, the air handling unit load in line with the air conditioning cooling load was predicted. The experimental results show that for the combination of operating parameters with the least power consumption in line with the cooling load obtained through genetic algorithm search, the power consumption of the air conditioning systems under said combination of operating parameters was reduced by 22% compared to the fixed operating parameters, thus indicating significant energy efficiency.

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Long Term Load Forecasting and Recommendations for China Based on Support Vector Regression

Cited by 12 publications

References 16 publications

Load Forecasting Techniques for Power System: Research Challenges and Survey

Load Forecasting Techniques for Power System: Research Challenges and Survey

A Comprehensive Review of the Load Forecasting Techniques Using Single and Hybrid Predictive Models

Support Vector Regression and Genetic Algorithm for HVAC Optimal Operation

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