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- NILM techniques for intelligent home energy management and ambient assisted living: a reviewPublication . Ruano, Antonio; Hernandez, Alvaro; Ureña, Jesus; Ruano, Maria; Garcia, JuanThe ongoing deployment of smart meters and different commercial devices has made electricity disaggregation feasible in buildings and households, based on a single measure of the current and, sometimes, of the voltage. Energy disaggregation is intended to separate the total power consumption into specific appliance loads, which can be achieved by applying Non-Intrusive Load Monitoring (NILM) techniques with a minimum invasion of privacy. NILM techniques are becoming more and more widespread in recent years, as a consequence of the interest companies and consumers have in efficient energy consumption and management. This work presents a detailed review of NILM methods, focusing particularly on recent proposals and their applications, particularly in the areas of Home Energy Management Systems (HEMS) and Ambient Assisted Living (AAL), where the ability to determine the on/off status of certain devices can provide key information for making further decisions. As well as complementing previous reviews on the NILM field and providing a discussion of the applications of NILM in HEMS and AAL, this paper provides guidelines for future research in these topics.
- An efficient parallel implementation of a least squares problemPublication . Ruano, Antonio; Fleming, P. J.; Jones, D. I.Least squares solutions are a very important problem, which appear in a broad range of disciplines (for instance, control systems, statistics, signal processing). Our interest in this kind of problems lies in their use of training neural network controllers.
- Single and multi-objective genetic programming design for B-spline neural networks and neuro-fuzzy systemsPublication . Cabrita, Cristiano Lourenço; Ruano, Antonio; Fonseca, C. M.The design phase of B-spline neural networks and neuro-fuzzy systems is a highly computationally complex task. Existent heuristics, namely the ASMOD algorithm, have been found to be highly dependent on the initial conditions employed. A Genetic Programming approach is proposed, which produces an efficient topology search, obtaining additionally more consistent solutions. The facility to incorporate a multi-objective approach to the GP algorithm is exploited, enabling the designer to obtain better conditioned models, and more adequate for their intended use.
- Applications of neural networks to control systemsPublication . Ruano, AntonioThis work investigates the applicability of artificial neural networks to control systems. The following properties of neural networks are identified as of major interest to this field: their ability to implement nonlinear mappings, their massively parallel structure and their capacity to adapt. Exploiting the first feature, a new method is proposed for PID autotuning. Based on integral measures of the open or closed loop step response, multilayer perceptrons (MLPs) are used to supply PID parameter values to a standard PID controller. Before being used on-line, the MLPs are trained offline, to provide PID parameter values based on integral performance criteria. Off-line simulations, where a plant with time-varying parameters and time varying transfer function is considered, show that well damped responses are obtained. The neural PID autotuner is subsequently implemented in real-time. Extensive experimentation confirms the good results obtained in the off-line simulations. To reduce the training time incurred when using the error back-propagation algorithm, three possibilities are investigated. A comparative study of higherorder methods of optimization identifies the Levenberg-Marquardt (LM)algorithm as the best method. When used for function approximation purposes, the neurons in the output layer of the MLPs have a linear activation function. Exploiting this linearity, the standard training criterion can be replaced by a new, yet equivalent, criterion. Using the LM algorithm to minimize this new criterion, together with an alternative form of Jacobian matrix, a new learning algorithm is obtained. This algorithm is subsequently parallelized. Its main blocks of computation are identified, separately parallelized, and finally connected together. The training time of MLPs is reduced by a factor greater than 70 executing the new learning algorithm on 7 Inmos transputers.
- Seismic event detection with artificial neural networksPublication . Madureira, G.; Ruano, AntonioThis experimental study focuses on a detection system at the seismic station level that should have a similar role to the detection algorithms based on the ratio STA/LTA. We tested two types of neural network: Multi-Layer Perceptrons and Support Vector Machines, trained in supervised mode. The universe of data consisted of 2903 patterns extracted from records of the PVAQ station, of the seismography network of the Institute of Meteorology of Portugal. The spectral characteristics of the records and its variation in time were reflected in the input patterns, consisting in a set of values of power spectral density in selected frequencies, extracted from a spectrogram calculated over a segment of record of pre-determined duration. The universe of data was divided, with about 60% for the training and the remainder reserved for testing and validation. To ensure that all patterns in the universe of data were within the range of variation of the training set, we used an algorithm to separate the universe of data by hyper-convex polyhedrons, determining in this manner a set of patterns that have a mandatory part of the training set. Additionally, an active learning strategy was conducted, by iteratively incorporating poorly classified cases in the training set. The best results, in terms of sensitivity and selectivity in the whole data ranged between 98% and 100%. These results compare very favorably with the ones obtained by the existing detection system, 50%.
- A neural network PID autotunerPublication . Ruano, Antonio; Lima, João; Mamat, R.; Fleming, P. J.Proportional, Integral and Derivative (PID) regulators are standard building blocks for industrial automation. Their popularity comes from their rebust performance and also from their functional simplicity. Whether because the plant is time-varying, or because of components ageing, these controllers need to be regularly retuned.
- Estimation and prediction of cloudiness from ground-based all-sky hemispherical digital imagesPublication . Martins, I.; Ferreira, P. M.; Ruano, AntonioCloudiness is the environmental factor most affecting the solar radiation reaching a particular location on the Earth surface. Therefore it has great impact on the performance of predictive solar radiation models for that location. This work aims contributing to the improvement of such models by using ground-to-sky hemispherical colour digital images as a means to estimate the fraction of visible sky corresponding to clouds and to clear sky, and by using radial basis function neural networks to model and predict the estimated cloudiness time-series. The general approach for cloudiness estimation, common to many image processing applications, consists in finding one threshold on a given pixel intensity scale that segments the image pixels into clear sky and cloud. The neural network models, selected by means of multiobjective genetic algorithms, are trained as one-step-ahead predictors and used iteratively in order to predict the cloudiness time-series up to a required prediction horizon. In order to allow the evaluation and comparison of image thresholding methods as well as forming a timeseries suitable to train and evaluate neural network models, the pixels of 410 images were manually classified as clear sky or cloud, establishing a reference database. Two well known image thresholding algorithms are tested and a neural network approach is presented. For the latter, a number of statistical measures are extracted from the images constituting a feature space of potential inputs. As well as for the cloudiness predictive models, the actual inputs and number of neurons to be employed are selected by means of a multi-objective genetic algorithm.
- Comparison of alternative approaches to neural network PID autotuningPublication . Ruano, Antonio; Lima, João; Mamat, R.; Fleming, P. J.In this paper, a scheme for the automatic tuning of PID controllers on-line, with the assistance of trained neural networks, is proposed. The alternative approaches are presented and compared.
- Fuzzy model identification by evolutionary, gradient based and memtic algorithmsPublication . Botzheim, J.; Kóczy, László T.; Ruano, AntonioOne of the crucial problems of fuzzy rule modeling is how to find an optimal or at least a quasi-optimal rule base fro a certain system. In most applications there is no human expert available, or, the result of a human expert's decision is too much subjective and is not reproducible, thus some automatic method to determine the fuzzy rule base must be deployed.
- Accelerating multi-objective control system design using a neuro-genetic approachPublication . Duarte, N. M.; Ruano, Antonio; Fonseca, C. M.; Fleming, P. J.Designing control systems using multiobjective genetic algorithms can lead to a substantial computational load as a result of the repeated evaluation of the multiple objectives and the population-based nature of the search. Here, a neural network approach, based on radial basis functions, is introduced to alleviate this problem by providing computationally inexpensive estimates of objective values during the search. A straightforward example demonstrates the utility of the approach.