Automatic irrigation scheduling systems are highly demanded in the agricultural sector due to their ability to both save water and manage deficit irrigation strategies. Elaborating a functional and efficient automatic irrigation system is a very complex task due to the high number of factors that the technician considers when managing irrigation in an optimal way. Automatic learning systems propose an alternative to traditional irrigation management by means of the automatic elaboration of predictions based on the learning of an agronomist (DSS). The aim of this paper is the study of several learning techniques in order to determine the goodness and error relative to expert decision. Nine orchards were tested during 2018 using linear regression (LR), random forest regression (RFR), and support vector regression (SVR) methods as engines of the irrigation decision support system (IDSS) proposed. The results obtained by the learning methods in three of these orchards have been compared with the decisions made by the agronomist over an entire year. The prediction model errors determined the best fitting regression model. The results obtained lead to the conclusion that these methods are valid engines to develop automatic irrigation scheduling systems.

Load forecasting models are of great importance in Electricity Markets and a wide range of techniques have been developed according to the objective being pursued. The increase of smart meters in different sectors (residential, commercial, universities, etc.) allows accessing the electricity consumption nearly in real time and provides those customers with large datasets that contain valuable information. In this context, supervised machine learning methods play an essential role. The purpose of the present study is to evaluate the effectiveness of using ensemble methods based on regression trees in short-term load forecasting. To illustrate this task, four methods (bagging, random forest, conditional forest, and boosting) are applied to historical load data of a campus university in Cartagena (Spain). In addition to temperature, calendar variables as well as different types of special days are considered as predictors to improve the predictions. Finally, a real application to the Spanish Electricity Market is developed: 48-h-ahead predictions are used to evaluate the economical savings that the consumer (the campus university) can obtain through the participation as a direct market consumer instead of purchasing the electricity from a retailer.

In this article, we give some results on the preservation of orderings between the components under the formation of coherent systems with different structures. We consider the stochastic, failure rate, reversed failure rate, and likelihood ratio orderings to compare two coherent structures formed from a set of components and from two sets of components, in both cases, when components are independent and either identically distributed or not necessarily identically distributed. As a consequence, we get new stochastic comparisons of systems with different k-out-of-n structures, and some results of partial orderings given by other authors may be also obtained from ours.

A low-cost handheld e-nose unit for assessing composition and quality of food in the field or for automatic discrimination of different foodstuffs would have significant commercial advantages and provide a positive impact on consumer safety and confidence. Whilst many studies have focused on the freshness of specific foodstuffs, the preliminary results presented in this report indicate that a low-cost (circa 30 euro) e-nose, based on MQ series gas sensors, using sinusoidally varying heater voltages, can successfully differentiate between samples of unspoiled lamb, hake, salmon, beef, pork and chicken, even using default, non-optimized control parameters, which could be applied in food adulteration and fraudulent labelling detection or in smart appliances to automatically select cooking parameters. Samples were exposed to the e-nose for five cycles of two minutes each and, by subjecting the response waveforms to Discrete Fourier Transform (DFT) analysis, harmonic component amplitudes were extracted. These data items were then fed into two different Machine Learning supervised classification methods (discriminant analysis and random forest) to determine which foodstuff was present. The relevance of the different DFT harmonic components and MQ sensors in the foodstuffs classification was quantified. Classification accuracies in excess of 95% were demonstrated.

Several measures of volatility have been developed in order to quantify the degree of uncertainty of an energy price series, which include historical volatility and price velocities, among others. This paper suggests using the permutation entropy, topological entropy and the modified permutation entropy as alternatives to measure volatility in energy markets. Simulated data show that these measures are more appropriate to quantify the uncertainty associated to a time series than those based on the standard deviation or other measures of dispersion. Finally, the proposed method is applied to some typical electricity markets: Nord Pool, Ontario, Omel and four Australian markets.