An evaluation of Bayesian techniques for controlling model complexity and selecting inputs in a neural network for short-term load forecasting

Hippert, Henrique S.; Taylor, James W.

doi:10.1016/j.neunet.2009.11.016

Cited by 82 publications

(42 citation statements)

References 25 publications

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“…Bayesian theory focuses on the probability distribution of weight in the weight space [17]. The conventional learning process is started with a suitable prior probability distribution of the output weights (…”

Section: Bayesian Echo State Network (Besn)mentioning

confidence: 99%

Echo State Network with Bayesian Regularization for Forecasting Short-Term Power Production of Small Hydropower Plants

et al. 2015

Energies

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Abstract:As a novel recurrent neural network (RNN), an echo state network (ESN) that utilizes a reservoir with many randomly connected internal units and only trains the readout, avoids increased complexity of training procedures faced by traditional RNN. The ESN can cope with complex nonlinear systems because of its dynamical properties and has been applied in hydrological forecasting and load forecasting. Due to the linear regression algorithm usually adopted by generic ESN to train the output weights, an ill-conditioned solution might occur, degrading the generalization ability of the ESN. In this study, the ESN with Bayesian regularization (BESN) is proposed for short-term power production forecasting of small hydropower (SHP) plants. According to the Bayesian theory, the weights distribution in space is considered and the optimal output weights are obtained by maximizing the posterior probabilistic distribution. The evidence procedure is employed to gain optimal hyperparameters for the BESN model. The recorded data obtained from the SHP plants in two different counties, located in Yunnan Province, China, are utilized to validate the proposed model. For comparison, the feed-forward neural networks with Levenberg-Marquardt algorithm (LM-FNN) and the generic ESN are also employed. The results indicate that BESN outperforms both LM-FNN and ESN.

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Section: Bayesian Echo State Network (Besn)mentioning

confidence: 99%

Echo State Network with Bayesian Regularization for Forecasting Short-Term Power Production of Small Hydropower Plants

et al. 2015

Energies

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“…Benefiting from its great extensibility, ANNs can integrate various other tools, including genetic algorithms [17], fuzzy logic [18], wavelet analysis [19], and grey systems [20]. The effectiveness of ANNs has been confirmed in several case studies [21,22]. Currently, ANNs are widely applied in various fields consisting of electrical loads forecasting, although ANNs are associated with the problems of slow convergence speed and the danger of easily falling into local minima.…”

Section: Introductionmentioning

confidence: 99%

Recurrence Interval Analysis on Electricity Consumption of an Office Building in China

2018

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Abstract:The energy management of office buildings has been a rising concern for owners, researchers, and energy suppliers. The volatility of power load in office buildings threatens energy consumption and risks device security. This paper investigates the load fluctuation patterns in an office building based on user data, using recurrence interval analysis for different thresholds. The recurrence intervals of volatility are fitted by stretched exponential distribution, from which the probability density function is derived. Then, the short-term and long-term memory effect on the fluctuations are learned by conditional probability density function and multifractal detrended fluctuation analysis, respectively. A hazard function is further established to analyze the risk estimation of load volatility and derive the value at risk (VaR). Thus, a functional relationship has been established between average recurrence interval and threshold. The methodology and analysis results addressed in this paper help to understand load fluctuation patterns and aid in the design of energy consumption strategies in office buildings. According to the results of our research, conclusions and management suggestions are provided at the end of this paper.

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“…The activation function used for the input and hidden layers is the function and, the output layer makes use a linear or function. FFNN have been widely used for load forecasting with success [2,16,17] due their ease of application with inputs from different sources and good performance.…”

Section: Feedforward Neural Networkmentioning

confidence: 99%

GA-ANN Short-Term Electricity Load Forecasting

Viegas

Vieira

Melício

et al. 2016

IFIP Advances in Information and Communication Technology

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An evaluation of Bayesian techniques for controlling model complexity and selecting inputs in a neural network for short-term load forecasting

Cited by 82 publications

References 25 publications

Echo State Network with Bayesian Regularization for Forecasting Short-Term Power Production of Small Hydropower Plants

Echo State Network with Bayesian Regularization for Forecasting Short-Term Power Production of Small Hydropower Plants

Recurrence Interval Analysis on Electricity Consumption of an Office Building in China

GA-ANN Short-Term Electricity Load Forecasting

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