Short term prediction of atmospheric temperature using neural networks

In this paper, a neural network based forecasting model for the maximum and the minimum temperature for the ground level is proposed. A backpropagation method of gradient-decent learning in multi-layer perceptron (MLP) type of neural network with only one hidden layer is considered. This network consists of 25 input nodes and two output nodes. The network is trained with a varying number of nodes in the hidden layer using a set of training sample and each of them is tested with a set of test sample. It accepts previous two consecutive days information (such as pressures, temperatures, relative humidities, etc.) as inputs for the estimation of the maximum and the minimum temperature as output. The network with 20 or less neurons in the hidden layer is found to be "optimum" and it produces an error within ±2° C for 80% of test cases.

Assessing Feature Importance for Short-Term Prediction of Electricity Demand in Medium-Voltage Loads

The design of new monitoring systems for intelligent distribution networks often requires both real-time measurements and pseudomeasurements to be processed. The former are obtained from smart meters, phasor measurement units and smart electronic devices, whereas the latter are predicted using appropriate algorithms—with the typical objective of forecasting the behaviour of power loads and generators. However, depending on the technique used for data encoding, the attempt at making predictions over a period of several days may trigger problems related to the high number of features. To contrast this issue, feature importance analysis becomes a tool of primary importance. This article is aimed at illustrating a technique devised to investigate the importance of features on data deemed relevant for predicting the next hour demand of aggregated, medium-voltage electrical loads. The same technique allows us to inspect the hidden layers of multilayer perceptrons entrusted with making the predictions, since, ultimately, the content of any hidden layer can be seen as an alternative encoding of the input data. The possibility of inspecting hidden layers can give wide support to researchers in a number of relevant tasks, including the appraisal of the generalisation capability reached by a multilayer perceptron and the identification of neurons not relevant for the prediction task.

Short-Term Prediction of Traffic State for a Rural Road Applying Ensemble Learning Process

Short-term prediction of traffic variables aims at providing information for travelers before commencing their trips. In this paper, machine learning methods consisting of long short-term memory (LSTM), random forest (RF), support vector machine (SVM), and K-nearest neighbors (KNN) are employed to predict traffic state, categorized into A to C for segments of a rural road network. Since the temporal variation of rural road traffic is irregular, the performance of applied algorithms varies among different time intervals. To find the most precise prediction for each time interval for segments, several ensemble methods, including voting methods and ordinal logit (OL) model, are utilized to ensemble predictions of four machine learning algorithms. The Karaj-Chalus rural road traffic data was used as a case study to show how to implement it. As there are many influential features on traffic state, the genetic algorithm (GA) has been used to identify 25 of 32 features, which are the most influential on models’ fitness. Results show that the OL model as an ensemble learning model outperforms machine learning models, and its accuracy is equal to 80.03 percent. The highest balanced accuracy achieved by OL for predicting traffic states A, B, and C is 89, 73.4, and 58.5 percent, respectively.

Prediction of the effective reproduction number of COVID-19 in Greece. A machine learning approach using Google mobility data

This paper demonstrates how a short-term prediction of the effective reproduction number (Rt) of COVID-19 in regions of Greece is achieved based on online mobility data. Various machine learning methods are applied to predict Rt and attribute importance analysis is performed to reveal the most important variables that affect the accurate prediction of Rt. Work and Park categories are identified as the most important mobility features when compared to the other attributes, with values of 0.25 and 0.24, respectively. Our results are based on an ensemble of diverse Rt methodologies to provide non-precautious and non-indulgent predictions. Random Forest algorithm achieved the highest R2 (0.8 approximately), Pearson’s and Spearman’s correlation values close to 0.9, outperforming in all metrics the other models. The model demonstrates robust results and the methodology overall represents a promising approach towards COVID-19 outbreak prediction. This paper can help health-related authorities when deciding on non-nosocomial interventions to prevent the spread of COVID-19.

A neural network model for short term prediction of surface ozone at Pone

A new method for short term prediction of air pollution is presented using the neural network technique, Due to increase in industrial and anthropogenic activity, air pollution is a serious subject of concern today, Surface ozone can be considered as a representative of total atmospheric oxidants and of air pollution, A three layer neural network model using the technique of adaptive pattern recognition is developed, The model can predict the mean surface ozone between 12 and 13 hours (hour of maximum concentration), The model can perform well both in training and independent periods, The classical methods of short term modelling are not reliable enough, The method can also be used for short term prediction of other air pollutants.