scholarly journals Probabilistic forecasting of coastal wave height during typhoon warning period using machine learning methods

2019 ◽  
Vol 21 (2) ◽  
pp. 343-358 ◽  
Author(s):  
Shien-Tsung Chen
Keyword(s):  
Machine Learning ◽  
Wave Height ◽  
Fuzzy Inference ◽  
Forecasting Model ◽  
Probabilistic Forecasting ◽  
Inference Model ◽  
Machine Learning Methods ◽  
Probabilistic Forecasts ◽  
Forecasting Method ◽  
Height Data

Abstract This study applied machine learning methods to perform the probabilistic forecasting of coastal wave height during the typhoon warning period. The probabilistic forecasts comprise a deterministic forecast and the probability distribution of a forecast error. A support vector machine was used to develop a real-time forecasting model for generating deterministic wave height forecasts. The forecast errors of deterministic forecasting were then used as a database to generate probabilistic forecasts by using the modified fuzzy inference model. The innovation of the modified fuzzy inference model includes calculating the similarity of the data by performing fuzzy implication and resampling the potential data from the fuzzy database for probability distribution. The probabilistic forecasting method was applied to the east coast of Taiwan, where typhoons frequently cause large waves. Hourly wave height data from an offshore buoy and various typhoon characteristics were used as inputs of the probabilistic forecasting model. Validation results from real typhoon events verified that the proposed probabilistic forecasting model can generate the predicted confidence interval, which can properly enclose the observed wave height data, excluding some cases with extreme wave heights. Moreover, an objective measure was used to validate the proposed probabilistic forecasting method.

scholarly journals Real-Time Probabilistic Flood Forecasting Using Multiple Machine Learning Methods

Water ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 787
Author(s):  
Dinh Ty Nguyen ◽  
Shien-Tsung Chen
Keyword(s):  
Machine Learning ◽  
Probability Distribution ◽  
Flood Forecasting ◽  
Support Vector ◽  
Forecasting Model ◽  
Forecast Errors ◽  
Uncertain Information ◽  
Probabilistic Forecasting ◽  
Learning Methods ◽  
Machine Learning Methods

Probabilistic flood forecasting, which provides uncertain information in the forecasting of floods, is practical and informative for implementing flood-mitigation countermeasures. This study adopted various machine learning methods, including support vector regression (SVR), a fuzzy inference model (FIM), and the k-nearest neighbors (k-NN) method, to establish a probabilistic forecasting model. The probabilistic forecasting method is a combination of a deterministic forecast produced using SVR and a probability distribution of forecast errors determined by the FIM and k-NN method. This study proposed an FIM with a modified defuzzification scheme to transform the FIM’s output into a probability distribution, and k-NN was employed to refine the probability distribution. The probabilistic forecasting model was applied to forecast flash floods with lead times of 1–3 hours in Yilan River, Taiwan. Validation results revealed the deterministic forecasting to be accurate, and the probabilistic forecasting was promising in view of a forecasted hydrograph and quantitative assessment concerning the confidence level.

scholarly journals Hybrid Forecasting Model for Short-Term Electricity Market Prices with Renewable Integration

2018 ◽  
Vol 11 (1) ◽  
pp. 57 ◽  
Author(s):  
Gerardo Osório ◽  
Mohamed Lotfi ◽  
Miadreza Shafie-khah ◽  
Vasco Campos ◽  
João Catalão
Keyword(s):  
Electricity Market ◽  
Wavelet Transforms ◽  
Fuzzy Inference ◽  
Electricity Sector ◽  
Forecasting Model ◽  
Probabilistic Forecasting ◽  
Short Term ◽  
Market Prices ◽  
Inference System ◽  
Hybrid Particle Swarm Optimization

In recent years, there have been notable commitments and obligations by the electricity sector for more sustainable generation and delivery processes to reduce the environmental footprint. However, there is still a long way to go to achieve necessary sustainability goals while ensuring standards of robustness and the quality of power grids. One of the main challenges hindering this progress are uncertainties and stochasticity associated with the electricity sector and especially renewable generation. In this paradigm shift, forecasting tools are indispensable, and their utilization can significantly improve system operation and minimize costs associated with all related activities. Thus, forecasting tools have an essential key role in all decision-making stages. In this work, a hybrid probabilistic forecasting model (HPFM) was developed for short-term electricity market prices (EMP) combining wavelet transforms (WT), hybrid particle swarm optimization (DEEPSO), adaptive neuro-fuzzy inference system (ANFIS), and Monte Carlo simulation (MCS). The proposed hybrid probabilistic forecasting model (HPFM) was tested and validated with real data from the Spanish and Pennsylvania-New Jersey-Maryland (PJM) markets. The proposed model exhibited favorable results and performance in comparison with previously published work considering electricity market prices (EMP) data, which is notable.

scholarly journals Rainfall forecasting model using machine learning methods: Case study Terengganu, Malaysia

2020 ◽  
Author(s):  
Wanie M. Ridwan ◽  
Michelle Sapitang ◽  
Awatif Aziz ◽  
Khairul Faizal Kushiar ◽  
Ali Najah Ahmed ◽  
...  
Keyword(s):  
Machine Learning ◽  
Forecasting Model ◽  
Rainfall Forecasting ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals Probabilistic photovoltaic power forecasting model based on deterministic forecasts

2020 ◽  
Vol 152 ◽  
pp. 01003
Author(s):  
L. Alfredo Fernandez-Jimenez ◽  
Sonia Terreros-Olarte ◽  
Pedro J. Zorzano-Santamaria ◽  
Montserrat Mendoza-Villena ◽  
Eduardo Garcia-Garrido
Keyword(s):  
Genetic Algorithm ◽  
Forecasting Model ◽  
Probabilistic Forecasting ◽  
Deterministic Models ◽  
Conditional Quantile ◽  
Normal Distributions ◽  
Probabilistic Forecasts ◽  
Mixture Of Normal Distributions ◽  
Quantile Regression Method ◽  
Power Forecasting

This paper presents an original probabilistic photovoltaic (PV) power forecasting model for the day-ahead hourly generation in a PV plant. The probabilistic forecasting model is based on 12 deterministic models developed with different techniques. An optimization process, ruled by a genetic algorithm, chooses the forecasts of the deterministic models in order to achieve the probability distribution function (PDF) for the PV generation in each one of the daylight hours of the following day in a parametric approach. The PDFs, which constitute the probabilistic forecasts, are a mixture of normal distributions, each one centred in the forecasts of the selected deterministic models. The genetic algorithm chooses the deterministic forecasts, the variance of the normal distributions and their weights in the mixture. In a case study the proposed model achieves better forecasting results than the obtained with the conditional quantile regression method applied to the same data used to develop the deterministic forecasting models.

scholarly journals Prediction of dynamic mooring responses of a floating wind turbine using an artificial neural network

2021 ◽  
Vol 1201 (1) ◽  
pp. 012023
Author(s):  
F A Bjørni ◽  
S Lien ◽  
T Aa Midtgarden ◽  
G Kulia ◽  
A Verma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Wind Turbine ◽  
Wave Height ◽  
Significant Wave Height ◽  
Learning Methods ◽  
Mooring Lines ◽  
Peak Period ◽  
Significant Wave ◽  
Machine Learning Methods ◽  
Artificial Neural

Abstract Numerical simulations in coupled aero-hydro-servo-elastic codes are known to be a challenge for design and analysis of offshore wind turbine systems because of the large number of design load cases involved in checking the ultimate and fatigue limit states. To alleviate the simulation burden, machine learning methods can be useful. This article investigates the effect of machine learning methods on predicting the mooring line tension of a spar floating wind turbine. The OC3 Hywind wind turbine with a spar-buoy foundation and three mooring lines is selected and simulated with SIMA. A total of 32 sea states with irregular waves are considered. Artificial neural works with different constructions were applied to reproduce the time history of mooring tensions. The best performing network provides a strong average correlation of 71% and consists of two hidden layers with 35 neurons, using the Bayesian regularisation backpropagation algorithm. Sea states applied in the network training are predicted with greater accuracy than sea states used for validation of the network. The correlation coefficient is primarily higher for sea states with lower significant wave height and peak period. One sea state with a significant wave height of 5 meters and a peak period of 9 seconds has an average extreme value deviation for all mooring lines of 0.46%. Results from the study illustrate the potential of incorporating artificial neural networks in the mooring design process.

scholarly journals Probabilistic reference model for hourly PV power generation forecasting

2020 ◽  
Vol 152 ◽  
pp. 01002
Author(s):  
L. Alfredo Fernandez-Jimenez ◽  
Sonia Terreros-Olarte ◽  
Alberto Falces ◽  
Pedro M. Lara-Santillan ◽  
Enrique Zorzano-Alba ◽  
...  
Keyword(s):  
Time Series ◽  
Reference Model ◽  
Power Production ◽  
Forecasting Model ◽  
Probabilistic Forecasting ◽  
Mean Power ◽  
Forecasting Models ◽  
Proposed Model ◽  
Probabilistic Forecasts

This paper presents a new probabilistic forecasting model of the hourly mean power production in a Photovoltaic (PV) plant. It uses the minimal information and it can provide probabilistic forecasts in the form of quantiles for the desired horizon, which ranges from the next hours to any day in the future. The proposed model only needs a time series of hourly mean power production in the PV plant, and it is intended to fill a gap in international literature where hardly any model has been proposed as a reference for comparison or benchmarking purposes with other probabilistic forecasting models. The performance of the proposed forecasting model is tested, in a case study, with the time series of hourly mean power production in a PV plant with 1.9 MW capacity. The results show an improvement with respect to the reference probabilistic PV power forecasting models reported in the literature.

scholarly journals Applying machine learning methods to avalanche forecasting

2008 ◽  
Vol 49 ◽  
pp. 107-113 ◽  
Author(s):  
A. Pozdnoukhov ◽  
R.S. Purves ◽  
M. Kanevski
Keyword(s):  
Machine Learning ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Support Vector ◽  
Avalanche Forecasting ◽  
Machine Learning Methods ◽  
Multiple Data ◽  
Complex Process ◽  
Probabilistic Forecasts ◽  
Vector Machines

AbstractAvalanche forecasting is a complex process involving the assimilation of multiple data sources to make predictions over varying spatial and temporal resolutions. Numerically assisted forecasting often uses nearest-neighbour methods (NN), which are known to have limitations when dealing with high-dimensional data. We apply support vector machines (SVMs) to a dataset from Lochaber, Scotland, UK, to assess their applicability in avalanche forecasting. SVMs belong to a family of theoretically based techniques from machine learning and are designed to deal with high-dimensional data. Initial experiments showed that SVMs gave results that were comparable with NN for categorical and probabilistic forecasts. Experiments utilizing the ability of SVMs to deal with high dimensionality in producing a spatial forecast show promise, but require further work.

scholarly journals Probabilistic Forecasting of Wind Turbine Icing Related Production Losses Using Quantile Regression Forests

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 158
Author(s):  
Jennie Molinder ◽  
Sebastian Scher ◽  
Erik Nilsson ◽  
Heiner Körnich ◽  
Hans Bergström ◽  
...  
Keyword(s):  
Machine Learning ◽  
Wind Energy ◽  
Quantile Regression ◽  
Weather Prediction ◽  
Machine Learning Method ◽  
Learning Method ◽  
Cold Climates ◽  
Probabilistic Forecasting ◽  
Probabilistic Forecasts ◽  
Probabilistic Machine Learning

A probabilistic machine learning method is applied to icing related production loss forecasts for wind energy in cold climates. The employed method, called quantile regression forests, is based on the random forest regression algorithm. Based on the performed tests on data from four Swedish wind parks available for two winter seasons, it has been shown to produce valuable probabilistic forecasts. Even with the limited amount of training and test data that were used in the study, the estimated forecast uncertainty adds more value to the forecast when compared to a deterministic forecast and a previously published probabilistic forecast method. It is also shown that the output from a physical icing model provides useful information to the machine learning method, as its usage results in an increased forecast skill when compared to only using Numerical Weather Prediction data. A potential additional benefit in machine learning for some stations was also found when using information in the training from other stations that are also affected by icing. This increases the amount of data, which is otherwise a challenge when developing forecasting methods for wind energy in cold climates.

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