An Advanced Bayesian Method for Short-Term Probabilistic Forecasting of the Generation of Wind Power

Bracale, Antonio; Falco, Pasquale De

doi:10.3390/en80910293

Cited by 31 publications

(12 citation statements)

References 54 publications

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“…Other diverse approaches for electricity price and load forecasting include Self-Organizing Map (SOM) [34], hybrid Principal Component Analysis (PCA) [35], Data Association Mining (DAM) [36], the Bayesian Method [37], Fuzzy Inference [38], Multiple Regression [36], Kernel Machine [39], Neural Networks [32,40], Particle Swarm Optimization (PSO) [41], etc.…”

Section: Related Workmentioning

confidence: 99%

Ensemble Prediction Model with Expert Selection for Electricity Price Forecasting

Neupane

Woon²,

Aung³

2017

Energies

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Abstract:Forecasting of electricity prices is important in deregulated electricity markets for all of the stakeholders: energy wholesalers, traders, retailers and consumers. Electricity price forecasting is an inherently difficult problem due to its special characteristic of dynamicity and non-stationarity. In this paper, we present a robust price forecasting mechanism that shows resilience towards the aggregate demand response effect and provides highly accurate forecasted electricity prices to the stakeholders in a dynamic environment. We employ an ensemble prediction model in which a group of different algorithms participates in forecasting 1-h ahead the price for each hour of a day. We propose two different strategies, namely, the Fixed Weight Method (FWM) and the Varying Weight Method (VWM), for selecting each hour's expert algorithm from the set of participating algorithms. In addition, we utilize a carefully engineered set of features selected from a pool of features extracted from the past electricity price data, weather data and calendar data. The proposed ensemble model offers better results than the Autoregressive Integrated Moving Average (ARIMA) method, the Pattern Sequence-based Forecasting (PSF) method and our previous work using Artificial Neural Networks (ANN) alone on the datasets for New York, Australian and Spanish electricity markets.

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Section: Related Workmentioning

confidence: 99%

Ensemble Prediction Model with Expert Selection for Electricity Price Forecasting

Neupane

Woon²,

Aung³

2017

Energies

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“…The BM provides a predictive PDF of the PV power applying the Bayesian inference of a data set of past observations [18,19].…”

Section: The Bayesian Forecasting Methodsmentioning

confidence: 99%

“…A new multi-model ensemble forecasting method (MEM) is proposed and is used to properly combine two probabilistic base predictors. The base predictors are a Bayesian-based method (BM) and a quantile regression-based method (QM); both were successfully used for the forecasting of PV power in the relevant literature [18][19][20][21]. Numerical applications were performed to validate the method on the basis of actual solar measurements; the performances of the proposed method are quantified numerically in terms of a proper score [i.e.…”

Section: Introductionmentioning

confidence: 99%

A New Ensemble Probabilistic Method for Short-Term Photovoltaic Power Forecasting

Bracale¹,

Carpinelli²,

Falco³

2016

Sustainable Energy - Technological Issues, Applications and Case Studies

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The high penetration of photovoltaic (PV) systems led to their growing impact on the planning and operation of actual distribution systems. However, the uncertainties due to the intermittent nature of solar energy complicate these tasks. Therefore, high-quality methods for forecasting the PV power are now essential, and many tools have been developed in order to provide useful and consistent forecasts. This chapter deals with probabilistic forecasting methods of PV system power, since they have recently drawn the attention of researchers as appropriate tools to cope with the unavoidable uncertainties of solar source. A new multi-model probabilistic ensemble is proposed; it properly combines a Bayesian-based and a quantile regression-based probabilistic method as individual predictors. Numerical applications based on actual irradiance data give evidence of the probabilistic performances of the proposed method in terms of both sharpness and calibration.

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“…Improving forecasts of power from solar panels, whether short-range forecasts out to a few hours, or longer, such as subseasonal forecasts, has been the subject of much research in recent years (as reviewed by [1][2][3][4]). The target uses of the forecasts include planning future installations, optimizing plant operations and efficiency, or balancing load demand and delivery [5]. As outlined by [6], the choice of forecasting technique varies with the decision and time-scale of interest (hourly, daily, and multiday).…”

Section: Introductionmentioning

confidence: 99%

Assessing Evidence for Weather Regimes Governing Solar Power Generation in Kuwait

et al. 2019

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With electricity representing around 20% of the global energy demand, and increasing support for renewable sources of electricity, there is also an escalating need to improve solar forecasts to support power management. While considerable research has been directed to statistical methods to improve solar power forecasting, few have employed finite mixture distributions. A statistically-objective classification of the overall sky condition may lead to improved forecasts. Combining information from the synoptic driving conditions for daily variability with local processes controlling subdaily fluctuations could assist with forecast validation and enhancement where few observations are available. Gaussian mixture models provide a statistical learning approach to automatically identify prevalent sky conditions (clear, semi-cloudy, and cloudy) and explore associated weather patterns. Here a first stage in the development of such a model is presented: examining whether there is sufficient information in the large-scale environment to identify days with clear, semi-cloudy, or cloudy conditions. A three-component Gaussian distribution is developed that reproduces the observed multimodal peaks in sky clearness indices, and their temporal distribution. Posterior probabilities from the fitted mixture distributions are used to identify periods of clear, partially-cloudy, and cloudy skies. Composites of low-level (850 hPa) humidity and winds for each of the mixture components reveal three patterns associated with the typical synoptic conditions governing the sky clarity, and hence, potential solar power.

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An Advanced Bayesian Method for Short-Term Probabilistic Forecasting of the Generation of Wind Power

Cited by 31 publications

References 54 publications

Ensemble Prediction Model with Expert Selection for Electricity Price Forecasting

Ensemble Prediction Model with Expert Selection for Electricity Price Forecasting

A New Ensemble Probabilistic Method for Short-Term Photovoltaic Power Forecasting

Assessing Evidence for Weather Regimes Governing Solar Power Generation in Kuwait

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