scholarly journals Machine learning techniques for daily solar energy prediction and interpolation using numerical weather models

2015 ◽  
Vol 28 (4) ◽  
pp. 1261-1274 ◽  
Author(s):  
R. Martin ◽  
R. Aler ◽  
J. M. Valls ◽  
I. M. Galvan
Keyword(s):  
Machine Learning ◽  
Solar Energy ◽  
Machine Learning Techniques ◽  
Energy Prediction ◽  
Numerical Weather ◽  
Numerical Weather Models ◽  
Learning Techniques ◽  
Weather Models

A Method for Wind Speed Forecasting in Airports Based on Nonparametric Regression

2014 ◽  
Vol 29 (6) ◽  
pp. 1332-1342 ◽  
Author(s):  
Pablo Rozas-Larraondo ◽  
Iñaki Inza ◽  
Jose A. Lozano
Keyword(s):  
Machine Learning ◽  
Wind Speed ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Numerical Weather ◽  
Area Of Interest ◽  
Numerical Weather Models ◽  
Weather Model ◽  
Learning Techniques ◽  
Weather Models

Abstract Wind is one of the parameters best predicted by numerical weather models, as it can be directly calculated from the physical equations of pressure that govern its movement. However, local winds are considerably affected by topography, which global numerical weather models, due to their limited resolution, are not able to reproduce. To improve the skill of numerical weather models, statistical and data analysis methods can be used. Machine learning techniques can be applied to train a model with data coming from both the model and observations in the area of interest. In this paper, a new method based on nonparametric multivariate locally weighted regression is studied for improving the forecasted wind speed of a numerical weather model. Wind direction data are used to build different regression models, as a way of accounting for the effect of surrounding topography. The use of this technique offers similar levels of accuracy for wind speed forecasts compared with other machine learning algorithms with the advantage of being more intuitive and easy to interpret.

scholarly journals A framework for predicting soft-fruit yields and phenology using embedded, networked microsensors, coupled weather models and machine-learning techniques

2019 ◽  
Author(s):  
Mark A. Lee ◽  
Angelo Monteiro ◽  
Andrew Barclay ◽  
Jon Marcar ◽  
Mirena Miteva-Neagu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Temperature ◽  
Machine Learning Techniques ◽  
Logistic Growth ◽  
Model Parameters ◽  
Spatial And Temporal Variability ◽  
Yield Data ◽  
Learning Techniques ◽  
Soft Fruit ◽  
Weather Models

AbstractPredicting harvest timing is a key challenge to sustainably develop soft fruit farming and reduce food waste. Soft fruits are perishable, high-value and seasonal, and sales prices are typically time-sensitive. In addition, fruit harvesting is labour-intensive and increasingly expensive making accurate phenological predictions valuable for growers. A novel approach for predicting soft fruit phenology and yields was developed and tested, using strawberries as the model crop. Seedlings were planted in polytunnels, and environmental and yield data were collected throughout the growing season. Over 1.2 million datapoints were collected by networked microsensors which measured spatial and temporal variability in air temperature, relative humidity (RH), soil moisture and photosynthetically active radiation (PAR). Fleeces were added to a subset of the plants to generate additional within-polytunnel variation. Cumulative fruit yields followed logistic growth curves and the coefficients of these curves were dependent on micro-climatic growing conditions. After 10,000 iterations, machine learning revealed that RH was the optimal factor informing the coefficients of these curves, perhaps because it is an integrative metric of air temperature and water status. Trigonometric models transformed weather forecasts, which showed a relatively low agreement with polytunnel air temperature (R2 = 0.6) and RH (R2 = 0.5) measurements, into more accurate polytunnel-specific predictions for temperature and RH (both R2 = 0.8). We present a framework for using machine-learning techniques to calculate curve coefficients and parametrise coupled weather models which can predict fruit yields and timing to a greater degree of accuracy that previously possible. Dataloggers measuring environmental and yield data could infer model parameters using iterative training for novel fruit varieties or crop types growing in different locations without a-priori phenological information. At this stage in the development of artificial intelligence and networked microsensors, this is a step forward in generating bespoke phenological prediction models to inform and support growers.

scholarly journals Can deep learning beat numerical weather prediction?

2021 ◽  
Vol 379 (2194) ◽  
pp. 20200097
Author(s):  
M. G. Schultz ◽  
C. Betancourt ◽  
B. Gong ◽  
F. Kleinert ◽  
M. Langguth ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Weather Prediction ◽  
Weather Data ◽  
Climate Modelling ◽  
Weather Forecasts ◽  
Numerical Weather ◽  
Numerical Weather Models ◽  
Weather And Climate ◽  
Weather Models

The recent hype about artificial intelligence has sparked renewed interest in applying the successful deep learning (DL) methods for image recognition, speech recognition, robotics, strategic games and other application areas to the field of meteorology. There is some evidence that better weather forecasts can be produced by introducing big data mining and neural networks into the weather prediction workflow. Here, we discuss the question of whether it is possible to completely replace the current numerical weather models and data assimilation systems with DL approaches. This discussion entails a review of state-of-the-art machine learning concepts and their applicability to weather data with its pertinent statistical properties. We think that it is not inconceivable that numerical weather models may one day become obsolete, but a number of fundamental breakthroughs are needed before this goal comes into reach. This article is part of the theme issue ‘Machine learning for weather and climate modelling’.

scholarly journals The Impact of Data Segmentation in Predicting Monthly Building Energy Use with Support Vector Regression

2021 ◽  
pp. 69-76
Author(s):  
William Mounter ◽  
Huda Dawood ◽  
Nashwan Dawood
Keyword(s):  
Machine Learning ◽  
Support Vector Regression ◽  
Energy Use ◽  
Building Energy ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Energy Usage ◽  
Energy Prediction ◽  
Learning Techniques

AbstractAdvances in metering technologies and machine learning methods provide both opportunities and challenges for predicting building energy usage in the both the short and long term. However, there are minimal studies on comparing machine learning techniques in predicting building energy usage on their rolling horizon, compared with comparisons based upon a singular forecast range. With the majority of forecasts ranges being within the range of one week, due to the significant increases in error beyond short term building energy prediction. The aim of this paper is to investigate how the accuracy of building energy predictions can be improved for long term predictions, in part of a larger study into which machine learning techniques predict more accuracy within different forecast ranges. In this case study the ‘Clarendon building’ of Teesside University was selected for use in using it’s BMS data (Building Management System) to predict the building’s overall energy usage with Support Vector Regression. Examining how altering what data is used to train the models, impacts their overall accuracy. Such as by segmenting the model by building modes (Active and dormant), or by days of the week (Weekdays and weekends). Of which it was observed that modelling building weekday and weekend energy usage, lead to a reduction of 11% MAPE on average compared with unsegmented predictions.

scholarly journals A framework for predicting soft-fruit yields and phenology using embedded, networked microsensors, coupled weather models and machine-learning techniques

2020 ◽  
Vol 168 ◽  
pp. 105103 ◽  
Author(s):  
Mark A. Lee ◽  
Angelo Monteiro ◽  
Andrew Barclay ◽  
Jon Marcar ◽  
Mirena Miteva-Neagu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Soft Fruit ◽  
Weather Models

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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