Efforts to understand the influence of historical climate change, at global and regional levels, have been increasing over the past decade. In particular, the estimates of air temperatures have been considered as a key factor in climate impact studies on agricultural, ecological, environmental, and industrial sectors. Accurate temperature prediction helps to safeguard life and property, playing an important role in planning activities for the government, industry, and the public. The primary aim of this study is to review the different machine learning strategies for temperature forecasting, available in the literature, presenting their advantages and disadvantages and identifying research gaps. This survey shows that Machine Learning techniques can help to accurately predict temperatures based on a set of input features, which can include the previous values of temperature, relative humidity, solar radiation, rain and wind speed measurements, among others. The review reveals that Deep Learning strategies report smaller errors (Mean Square Error = 0.0017 °K) compared with traditional Artificial Neural Networks architectures, for 1 step-ahead at regional scale. At the global scale, Support Vector Machines are preferred based on their good compromise between simplicity and accuracy. In addition, the accuracy of the methods described in this work is found to be dependent on inputs combination, architecture, and learning algorithms. Finally, further research areas in temperature forecasting are outlined.

Air Temperature Forecasting Using Machine Learning Techniques: A Review

Prediction of heat waves in Pakistan using quantile regression forests

Automatic estrus detection techniques in dairy cows have been present by different traits. Pedometers and accelerators are the most common sensor equipment. Most of the detection methods are associated with the supervised classification technique, which the training set becomes a crucial reference. The training set obtained by visual observation is subjective and time consuming. Another limitation of this approach is that it usually does not consider the factors affecting successful alerts, such as the discriminative figure, activity type of cows, the location and direction of the sensor node placed on the neck collar of a cow. This paper presents a novel estrus detection method that uses k-means clustering algorithm to create the training set online for each cow. And the training set is finally used to build an activity classification model by SVM. The activity index counted by the classification results in each sampling period can measure cow’s activity variation for assessing the onset of estrus. The experimental results indicate that the peak of estrus time are higher than that of non-estrus time at least twice in the activity index curve, and it can enhance the sensitivity and significantly reduce the error rate.

Estrus Detection in Dairy Cows from Acceleration Data using Self-learning Classification Models

Predictive maintenance models are thought to be a reliable alternative to costly on-site maintenance techniques in the solar photovoltaic industry. They provide the owners with a third-party system to objectively diagnose and prevent failures in the system or any of its components. These models depend on data acquisition and analysis to compare the estimated performance with the actual energy production of the system, to conclude on the systems' health status. Ambient temperature, cell temperature, and solar irradiance forecasting are crucial parameters in predictive maintenance models’ formulation. These parameters represent the input variables to the model and define its efficiency to a great extent. Nonetheless, the current approaches to predict weather-related parameters have a high dependency on data availability and, in some cases, sensibility to the location where they are being measured. Forecasting climate parameters using neighboring weather stations seems to be a promising option to tackle the cost of on-site sensing and data availability, however, the scarcity of applications of specialized forecasting methods to predict solar irradiance and temperature (cell and ambient) for such applications represents an obstacle. This document will present a review of the state-of-the-art comparing the techniques to forecast solar irradiance, and ambient and cell temperature, and their relationship with predictive maintenance models, by acquiring data from weather stations. This review evaluates the suitability of the models for solar photovoltaic applications, taking on their forecasting horizon, computational cost, and improvement opportunities to propose a framework for future work in the photovoltaic industry. 

Review of forecasting methods to support photovoltaic predictive maintenance

Climate change has caused a rise in temperature extremes, particularly heatwaves, in recent decades. Physical-empirical models are developed in this study using two classical machine learning algorithms, namely decision tree (DT) and random forests, and a novel hybrid technique consists of Ada-Boost Regression and decision tree (ABR-DT) for forecasting annual heatwave days (HWDs) of Iran from synoptic predictors. The daily temperature data of Princeton Meteorological Forcing were extracted for 27 points to estimate the annual number of HWDs, and the National Centers for Environmental Prediction reanalysis data were used as predictors. The major synoptic variables were extracted for four pressure levels (e.g., 300, 500, 850, and 1000 hPa) and three monthly time lags. The Principal Component Analysis was employed to reduce the diverse predictors and their features to the most optimal structure. The grid point-based performance evaluation revealed the superiority of ABR-DT, which showed a correlation coefficient (CC) of 0.860 and meant absolute error (MAE) of 6.929, using only specific humidity and wind component as predictors. The spatial performance indices over eight different climate regions of Iran also showed the better performance of ABR-DT, which improved the CC and MAE of its two alternatives up to 185 and 19%. The study identified the optimal parameter combination as the predictors of heatwaves by examining the effects of numerous weather components. The results proved the proposed hybrid forecasting approach’s effectiveness in predicting heatwave days, a devastating hazard, for many regions.

Prediction of heat waves using meteorological variables in diverse regions of Iran with advanced machine learning models

To predict the time series, a model combining the wavelet transform and support vector machine is set up. First, wavelet transform is applied to decompose the series into sub series with different time scales. Then, the SVM is applied to the sub series to simulate and predict future behavior. And then by the inverse wavelet transform, the series are reconstructed, which is the prediction for the time series. The prediction precision of the new model is higher than that of the SVM model and the artificial neural network model for many processes, such as runoff, precipitation, temperature. And the new Wavelet-SVM model is applied to analyze the month temperature time series of city Tangshan for example. The universal applicability of the new Wavelet-SVM model and the improvement direction are discussed in the end.

/pdf/prediction-of-temperature-time-series-based-on-wavelet-3vzfe1nm69.pdf

Prediction of Temperature Time Series Based on Wavelet Transform and Support Vector Machine

This paper discusses the second-order matrix eigenvalue problem by means of the nonlinearization of the Lax pairs, then the author gives the Bargmann and Neumann constraints of this problem. The relation between the potentials and the eigenfunction is set up based on these constraints. By means of the nonlinearization of the Lax pairs, the author found these systems of the eigenvalue problem can be equal to the Hamilton canonical system in real symplectic space. In the end, the infinite-dimensions Dynamical systems can be transformed into the finite-dimensions Hamilton canonical systems in the symplectic space. As well, this paper obtains the representations of the solutions for the evolution equations.

Bargmann and Neumann System of the Second-Order Matrix Eigenvalue Problem

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Prediction of Temperature Time Series Based on Wavelet Transform and Support Vector Machine

Bargmann and Neumann System of the Second-Order Matrix Eigenvalue Problem