An electric power generation forecasting method using support vector machine

Guo, Li; Chen, Jinhao; Wu, Fukui; Wang, Manran

doi:10.1080/21642583.2018.1544947

Cited by 10 publications

(2 citation statements)

References 13 publications

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“…Guo et al and Fu used a support vector model for electricity load [60,61]. Kavaklioglu et al derived electricity consumption as a function of socioeconomic indicators such as population, gross national product, and imports and exports [39].…”

Section: Support Vector Machinementioning

confidence: 99%

A Comparative Analysis of Machine Learning Approaches for Short-/Long-Term Electricity Load Forecasting in Cyprus

Solyalı

2020

Sustainability

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Estimating the electricity load is a crucial task in the planning of power generation systems and the efficient operation and sustainable growth of modern electricity supply networks. Especially with the advent of smart grids, the need for fairly precise and highly reliable estimation of electricity load is greater than ever. It is a challenging task to estimate the electricity load with high precision. Many energy demand management methods are used to estimate future energy demands correctly. Machine learning methods are well adapted to the nature of the electrical load, as they can model complicated nonlinear connections through a learning process containing historical data patterns. Many scientists have used machine learning (ML) to anticipate failure before it occurs as well as predict the outcome. ML is an artificial intelligence (AI) subdomain that involves studying and developing mathematical algorithms to understand data or obtain data directly without relying on a prearranged model algorithm. ML is applied in all industries. In this paper, machine learning strategies including artificial neural network (ANN), multiple linear regression (MLR), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM) were used to estimate electricity demand and propose criteria for power generation in Cyprus. The simulations were adapted to real historical data explaining the electricity usage in 2016 and 2107 with long-term and short-term analysis. It was observed that electricity load is a result of temperature, humidity, solar irradiation, population, gross national income (GNI) per capita, and the electricity price per kilowatt-hour, which provide input parameters for the ML algorithms. Using electricity load data from Cyprus, the performance of the ML algorithms was thoroughly evaluated. The results of long-term and short-term studies show that SVM and ANN are comparatively superior to other ML methods, providing more reliable and precise outcomes in terms of fewer estimation errors for Cyprus’s time series forecasting criteria for power generation.

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Section: Support Vector Machinementioning

confidence: 99%

A Comparative Analysis of Machine Learning Approaches for Short-/Long-Term Electricity Load Forecasting in Cyprus

Solyalı

2020

Sustainability

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“…However, since the efficiency of green power is largely impacted by the environment, the stability of the overall power supply remains uncertain [1], [2], [3]. Due to the essential role that electric power supply plays in a nations' economic development, developing an effective power supply forecasting schemes has been one of the top priorities [4], [5].…”

Section: Introductionmentioning

confidence: 99%

An Interval-Valued Time Series Forecasting Scheme With Probability Distribution Features for Electric Power Generation Prediction

Chang

Lee

2022

IEEE Access

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Developing an effective interval-valued time series (ITS) forecasting scheme for electric power generation is an important issue for energy operators and governments when making energy strategic decisions. The existing studies for ITS forecasting only consider basic descriptive information such as center, radius, upper and lower bounds, and overlooks the distribution information within the data interval. In this study, an interval-valued time series forecasting scheme based on probability distribution information features of interval-valued data with machine learning algorithms is proposed to enhance electric power generation forecasting. In the proposed scheme, the central tendency features and dispersion features from the interval-valued data are designed as integrated features sets (IFS) and used as predictor variables. Three methods including supper vector regression and extreme learning machine and multivariate adaptive regression splines based on the IFS are utilized to develop ITS forecasting models. The daily time series of the metered generation from the Australian Energy Market Operator is used to illustrate the proposed scheme. Empirical results show that the proposed ITS forecasting schemes with IFS outperform the eight benchmark models and thus validate that the proposed scheme is an effective alternative for interval-valued electric power generation forecasting.

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Analysis of Climate-Net Unit Generated (NUG) Relationship for the Hydroelectric Power Station

Azman

Tukimat

Malek

2019

Water Resources Development and Management

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An electric power generation forecasting method using support vector machine

Cited by 10 publications

References 13 publications

A Comparative Analysis of Machine Learning Approaches for Short-/Long-Term Electricity Load Forecasting in Cyprus

A Comparative Analysis of Machine Learning Approaches for Short-/Long-Term Electricity Load Forecasting in Cyprus

An Interval-Valued Time Series Forecasting Scheme With Probability Distribution Features for Electric Power Generation Prediction

Analysis of Climate-Net Unit Generated (NUG) Relationship for the Hydroelectric Power Station

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