A two-step short-term probabilistic wind forecasting methodology based on predictive distribution optimization

Sun, Mucun; Feng, Cong; Chartan, Erol Kevin; Hodge, Bri‐Mathias; Zhang, Jie

doi:10.1016/j.apenergy.2019.01.182

Cited by 44 publications

(9 citation statements)

References 18 publications

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“…117 The probabilistic wind power forecasting using Q-learning technique enhances the accuracy by conditional aggregation and Gaussian mixture model for fitting the probability density functions, 118 support vector regression surrogation model is used for deterministic wind power forecast. 119 Figure 9 shows the architecture of DBNs by stacking RBMs. DBN is a special form of the Bayesian probabilistic generative model and consists of many layers of stochastic and hidden variables.…”

Section: Probabilistic Modelsmentioning

confidence: 99%

A holistic review on energy forecasting using big data and deep learning models

Devaraj¹,

Elavarasan

Shafiullah

et al. 2021

Int J Energy Res

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With the growth of forecasting models, energy forecasting is used for better planning, operation, and management in the electric grid. It is important to improve the accuracy of forecasting for a faster decision-making process. Big data can handle large scale of datasets and extract the patterns fed to the deep learning models that improve the accuracy than the traditional models and hence, recently started its application in energy forecasting. In this study, an in-depth insight is initially derived by investigating artificial intelligence (AI) and machine learning (ML) techniques with their strengths and weaknesses, enhancing the consistency of renewable energy integration and modernizing the overall grid. However, Deep learning (DL) algorithms have the capability to handle big data by capturing the inherent non-linear features through automatic feature extraction methods. Hence, an extensive and exhaustive review of generative, hybrid, and discriminative DL models is being examined for short-term, medium-term, and long-term forecasting of renewable energy, energy consumption, demand, and supply etc. This study also explores the different data decomposition strategies used to build forecasting models. The recent success of DL is being investigated, and the insights of paradoxes in parameter optimization during the training of the model are identified. The impact of weather prediction in the wind and solar energy forecasting is examined in detail. From the existing literatures, it has seen that the average mean absolute percentage error (MAPE) value of solar and wind energy forecasting is 10.29% and 6.7% respectively. Current technology barriers involved in implementing these models for energy forecasting and the recommendations to overcome the existing system barriers are identified. An indepth analysis, discussions of the results, and the scope for improvement are provided in this study including the potential directions for future research in the energy forecasting.

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Section: Probabilistic Modelsmentioning

confidence: 99%

A holistic review on energy forecasting using big data and deep learning models

Devaraj¹,

Elavarasan

Shafiullah

et al. 2021

Int J Energy Res

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show abstract

“…Forecasting [24][25], [27][28][29][30][31], [35][36][37][38], [40][41][42][43], [45][46], [49][50][51], [54], [57][58] Storage systems [27][28][29], [31], [35], [37], [41], [46], [49][50][51], [54], [57], [62] Risk management [24], [27][28], [45], [50][51], [58], [59] Electric vehicles [24][25], [27], [35], [45], [49],…”

Section: Reviewed Workmentioning

confidence: 99%

A review of short‐term wind power probabilistic forecasting and a taxonomy focused on input data

Bazionis

Karafotis

Georgilakis

2021

IET Renewable Power Gen

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A review of state‐of‐the‐art short‐term wind power probabilistic forecasting models is the focus here. The improvement of the accuracy and efficiency of probabilistic forecasting models has been in the centre of attention of researchers in recent years, since the need to further comprehend and efficiently use the uncertainty of forecasts is increasing. Since the optimal operation and control of energy systems and electricity markets is one of the important aspects of performing wind power forecasts, this review focuses in short‐term probabilistic forecasting models, which could prove to be useful in the daily planning and operation of power systems. The short‐term concept of forecasts is analysed in detail, along with the case studies and examples proposed by the reviewed literature. The key advantages and disadvantages of the reviewed probabilistic forecasting methodologies are identified. Furthermore, different classifications of the reviewed works according to the data that are used to provide an accurate forecasting model are also provided. Future directions in the field of short‐term wind power probabilistic forecasting are also proposed.

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“…Classifying each day is more logical than assuming seasonality in the data, since within each season, there will surely be days with different weather behaviors. Due to the purpose of the PLF being to obtain the demand/consumption of a specific day (short-term load forecasting) [ 56 , 57 ], this time period was used to obtain both the KDE function and its uncertainty map based on both quantitative and qualitative characteristics. Thus, when a load forecasting is made, it is first characterized in order to select the characterization criteria of the training days that will construct its uncertainty map.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Load Forecasting Optimization for Building Energy Models via Day Characterization

Segarra

Ruiz

Bandera

2021

Sensors

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Accurate load forecasting in buildings plays an important role for grid operators, demand response aggregators, building energy managers, owners, customers, etc. Probabilistic load forecasting (PLF) becomes essential to understand and manage the building’s energy-saving potential. This research explains a methodology to optimize the results of a PLF using a daily characterization of the load forecast. The load forecast provided by a calibrated white-box model and a real weather forecast was classified and hierarchically selected to perform a kernel density estimation (KDE) using only similar days from the database characterized quantitatively and qualitatively. A real case study is presented to show the methodology using an office building located in Pamplona, Spain. The building monitoring, both inside—thermal sensors—and outside—weather station—is key when implementing this PLF optimization technique. The results showed that thanks to this daily characterization, it is possible to optimize the accuracy of the probabilistic load forecasting, reaching values close to 100% in some cases. In addition, the methodology explained is scalable and can be used in the initial stages of its implementation, improving the values obtained daily as the database increases with the information of each new day.

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A two-step short-term probabilistic wind forecasting methodology based on predictive distribution optimization

Cited by 44 publications

References 18 publications

A holistic review on energy forecasting using big data and deep learning models

A holistic review on energy forecasting using big data and deep learning models

A review of short‐term wind power probabilistic forecasting and a taxonomy focused on input data

Probabilistic Load Forecasting Optimization for Building Energy Models via Day Characterization

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