A hierarchical neural model with time windows in long-term electrical load forecasting

Carpinteiro, O.A.S.; Lima, Isaias; Leme, Rafael Coradi; Souza, Antônio Carlos Zambroni de; Moreira, Edmilson M.; Pinheiro, Carlos A. M.

doi:10.1007/s00521-006-0072-8

Cited by 9 publications

(4 citation statements)

References 15 publications

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“…Let us call such training points as ''neighborhood training points'' of x. Their number is defined as n tr , and the average distance from them to x is measured (9).…”

Section: A Deterministic Generation Of Representative and Balanced Trmentioning

confidence: 99%

“…Artificial neural networks (ANNs) have received increasing research interest in time series forecasting tasks in recent years [1][2][3][4][5][6][7][8][9][10][11][12]. RBFNNs [13] are one of the most widely applied ANNs for this type of tasks due to their simple architecture and learning scheme and the possibility of incorporating the qualitative aspects of human experience into the model selection and training [5,6].…”

Section: Introductionmentioning

confidence: 99%

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A deterministic model selection scheme for incremental RBFNN construction in time series forecasting

Florido

Pomares

Rojas

et al. 2010

Neural Comput & Applic

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This paper presents a fast and new deterministic model selection methodology for incremental radial basis function neural network (RBFNN) construction in time series prediction problems. The development of such special designed methodology is motivated by the problems that arise when using a K-fold cross-validation-based model selection methodology for this paradigm: its random nature and the subjective decision for a proper value of K, resulting in large bias for low values and high variance and computational cost for high values. Taking into account these drawbacks, the proposed model selection approach is a combined algorithm that takes advantage of two balanced and representative training and validation sets for their use in RBFNN initialization, optimization and network model evaluation. This way, the model prediction accuracy is improved, getting small variance and bias, reducing the computation time spent in selecting the model and avoiding random and computationally expensive model selection methodologies based on K-fold cross-validation procedures.

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“…Let us call such training points as ''neighborhood training points'' of x. Their number is defined as n tr , and the average distance from them to x is measured (9).…”

Section: A Deterministic Generation Of Representative and Balanced Trmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A deterministic model selection scheme for incremental RBFNN construction in time series forecasting

Florido

Pomares

Rojas

et al. 2010

Neural Comput & Applic

View full text Add to dashboard Cite

show abstract

“…Parametric methods construct a statistical model of load by mining the qualitative relationships between load and factors affecting load. These methods, such as multiple linear regression, autoregressive and moving average, need assumed parameters estimated from historical data [3][4][5]. Usually they can't deal with nonlinear or random relationships between load and factors affecting load.…”

Section: Introductionmentioning

confidence: 99%

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

Ye¹,

Zhu²,

Xiao³

2012

EPE

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Long-term load forecasting (LTLF) is a challenging task because of the complex relationships between load and factors affecting load. However, it is crucial for the economic growth of fast developing countries like China as the growth rate of gross domestic product (GDP) is expected to be 7.5%, according to China’s 11th Five-Year Plan (2006-2010). In this paper, LTLF with an economic factor, GDP, is implemented. A support vector regression (SVR) is applied as the training algorithm to obtain the nonlinear relationship between load and the economic factor GDP to improve the accuracy of forecasting

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“…However, its inability to reflect nonlinear relationships between the dependent variable and each explicative variable makes it very little value in dealing with strata movement. Recently, artificial neural network has been increasingly employed as an effective tool for modeling complicated problems, in which the governing equations are difficult to be defined [3][4][5][6][7]. Many studies have explored the applications of artificial neural network to complex geotechnical engineering system.…”

Section: Introductionmentioning

confidence: 99%

Neural network committee-based sensitivity analysis strategy for geotechnical engineering problems

Cao

Qiao

2007

Neural Comput Applic

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Neural network usually acts as a ''black box'' in diverse fields to perform prediction, classification, and regression. Different from the conventional usages, neural network is herein attempted to handle factor sensitivity analysis in a geotechnical engineering system. After systematically investigating instability of employing single neural network in factor sensitivity analysis, a neural network committee (NNC)-based sensitivity analysis strategy is first algorithmically presented based on the particular mathematical ideas of weak law of large numbers in probability and optimization. Significantly, this study especially emphasizes the practical application of the NNC-based sensitivity analysis strategy to highlight the mechanism underlying in strata movement. The principal goal is to reveal the relationships among influential factors on strata movement through estimating the relative contribution of each explicative (input) variable on dependent (output) variables of strata movement. It is demonstrated that the NNC-based sensitivity analysis strategy rationally not only reveals the relative contribution of each explicative variable on dependent variables but also indicates the predictability of each dependent variable. In addition, an improved prediction model is resulted from integrating the sensitivity analysis results into neural network modeling, and it is capable of facilitating the convergence training of neural network model and advancing its prediction precision on strata movement angles. The above outcomes indicate that the NNC-based sensitivity analysis strategy provides a new paradigm of applying neural networks to deal with complex geotechnical engineering problems.

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A hierarchical neural model with time windows in long-term electrical load forecasting

Cited by 9 publications

References 15 publications

A deterministic model selection scheme for incremental RBFNN construction in time series forecasting

A deterministic model selection scheme for incremental RBFNN construction in time series forecasting

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

Neural network committee-based sensitivity analysis strategy for geotechnical engineering problems

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