A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

Agrupamiento, predicción y clasificación ordinal para series temporales utilizando técnicas de machine learning: aplicaciones

In the last years, there has been an increase in the number of fields improving their standard processes by using machine learning (ML) techniques. The main reason for this is that the vast amount of data generated by these processes is difficult to be processed by humans. Therefore, the development of automatic methods to process and extract relevant information from these data processes is of great necessity, giving that these approaches could lead to an increase in the economic benefit of enterprises or to a reduction in the workload of some current employments. Concretely, in this Thesis, ML approaches are applied to problems concerning time series data. Time series is a special kind of data in which data points are collected chronologically. Time series are present in a wide variety of fields, such as atmospheric events or engineering applications. Besides, according to the main objective to be satisfied, there are different tasks in the literature applied to time series. Some of them are those on which this Thesis is mainly focused: clustering, classification, prediction and, in general, analysis. Generally, the amount of data to be processed is huge, arising the need of methods able to reduce the dimensionality of time series without decreasing the amount of information. In this sense, the application of time series segmentation procedures dividing the time series into different subsequences is a good option, given that each segment defines a specific behaviour. Once the different segments are obtained, the use of statistical features to characterise them is an excellent way to maximise the information of the time series and simultaneously reducing considerably their dimensionality. In the case of time series clustering, the objective is to find groups of similar time series with the idea of discovering interesting patterns in time series datasets. In this Thesis, we have developed a novel time series clustering technique. The aim of this proposal is twofold: to reduce as much as possible the dimensionality and to develop a time series clustering approach able to outperform current state-of-the-art techniques. In this sense, for the first objective, the time series are segmented in order to divide the them identifying different behaviours. Then, these segments are projected into a vector of statistical features aiming to reduce the dimensionality of the time series. Once this preprocessing step is done, the clustering of the time series is carried out, with a significantly lower computational load. This novel approach has been tested on all the time series datasets available in the University of East Anglia and University of California Riverside (UEA/UCR) time series classification (TSC) repository. Regarding time series classification, two main paths could be differentiated: firstly, nominal TSC, which is a well-known field involving a wide variety of proposals and transformations applied to time series. Concretely, one of the most popular transformation is the shapelet transform (ST), which has been widely used in this field. The original method extracts shapelets from the original time series and uses them for classification purposes. Nevertheless, the full enumeration of all possible shapelets is very time consuming. Therefore, in this Thesis, we have developed a hybrid method that starts with the best shapelets extracted by using the original approach with a time constraint and then tunes these shapelets by using a convolutional neural network (CNN) model. Secondly, time series ordinal classification (TSOC) is an unexplored field beginning with this Thesis. In this way, we have adapted the original ST to the ordinal classification (OC) paradigm by proposing several shapelet quality measures taking advantage of the ordinal information of the time series. This methodology leads to better results than the state-of-the-art TSC techniques for those ordinal time series datasets. All these proposals have been tested on all the time series datasets available in the UEA/UCR TSC repository. With respect to time series prediction, it is based on estimating the next value or values of the time series by considering the previous ones. In this Thesis, several different approaches have been considered depending on the problem to be solved. Firstly, the prediction of low-visibility events produced by fog conditions is carried out by means of hybrid autoregressive models (ARs) combining fixed-size and dynamic windows, adapting itself to the dynamics of the time series. Secondly, the prediction of convective cloud formation (which is a highly imbalance problem given that the number of convective cloud events is much lower than that of non-convective situations) is performed in two completely different ways: 1) tackling the problem as a multi-objective classification task by the use of multi-objective evolutionary artificial neural networks (MOEANNs), in which the two conflictive objectives are accuracy of the minority class and the global accuracy, and 2) tackling the problem from the OC point of view, in which, in order to reduce the imbalance degree, an oversampling approach is proposed along with the use of OC techniques. Thirdly, the prediction of solar radiation is carried out by means of evolutionary artificial neural networks (EANNs) with different combinations of basis functions in the hidden and output layers. Finally, the last challenging problem is the prediction of energy flux from waves and tides. For this, a multitask EANN has been proposed aiming to predict the energy flux at several prediction time horizons (from 6h to 48h). All these proposals and techniques have been corroborated and discussed according to physical and atmospheric models. The work developed in this Thesis is supported by 11 JCR-indexed papers in international journals (7 Q1, 3 Q2, 1 Q3), 11 papers in international conferences, and 4 papers in national conferences

Time series data mining: preprocessing, analysis, segmentation and prediction. Applications

Currently, the amount of data which is produced for any information system is increasing exponentially. This motivates the development of automatic techniques to process and mine these data correctly. Specifically, in this Thesis, we tackled these problems for time series data, that is, temporal data which is collected chronologically. This kind of data can be found in many fields of science, such as palaeoclimatology, hydrology, financial problems, etc. TSDM consists of several tasks which try to achieve different objectives, such as, classification, segmentation, clustering, prediction, analysis, etc. However, in this Thesis, we focus on time series preprocessing, segmentation and prediction. Time series preprocessing is a prerequisite for other posterior tasks: for example, the reconstruction of missing values in incomplete parts of time series can be essential for clustering them. In this Thesis, we tackled the problem of massive missing data reconstruction in SWH time series from the Gulf of Alaska. It is very common that buoys stop working for different periods, what it is usually related to malfunctioning or bad weather conditions. The relation of the time series of each buoy is analysed and exploited to reconstruct the whole missing time series. In this context, EANNs with PUs are trained, showing that the resulting models are simple and able to recover these values with high precision. In the case of time series segmentation, the procedure consists in dividing the time series into different subsequences to achieve different purposes. This segmentation can be done trying to find useful patterns in the time series. In this Thesis, we have developed novel bioinspired algorithms in this context. For instance, for paleoclimate data, an initial genetic algorithm was proposed to discover early warning signals of TPs, whose detection was supported by expert opinions. However, given that the expert had to individually evaluate every solution given by the algorithm, the evaluation of the results was very tedious. This led to an improvement in the body of the GA to evaluate the procedure automatically. For significant wave height time series, the objective was the detection of groups which contains extreme waves, i.e. those which are relatively large with respect other waves close in time. The main motivation is to design alert systems. This was done using an HA, where an LS process was included by using a likelihood-based segmentation, assuming that the points follow a beta distribution. Finally, the analysis of similarities in different periods of European stock markets was also tackled with the aim of evaluating the influence of different markets in Europe. When segmenting time series with the aim of reducing the number of points, different techniques have been proposed. However, it is an open challenge given the difficulty to operate with large amounts of data in different applications. In this work, we propose a novel statistically-driven CRO algorithm (SCRO), which automatically adapts its parameters during the evolution, taking into account the statistical distribution of the population fitness. This algorithm improves the state-of-the-art with respect to accuracy and robustness. Also, this problem has been tackled using an improvement of the BBPSO algorithm, which includes a dynamical update of the cognitive and social components in the evolution, combined with mathematical tricks to obtain the fitness of the solutions, which significantly reduces the computational cost of previously proposed coral reef methods. Also, the optimisation of both objectives (clustering quality and approximation quality), which are in conflict, could be an interesting open challenge, which will be tackled in this Thesis. For that, an MOEA for time series segmentation is developed, improving the clustering quality of the solutions and their approximation. The prediction in time series is the estimation of future values by observing and studying the previous ones. In this context, we solve this task by applying prediction over high-order representations of the elements of the time series, i.e. the segments obtained by time series segmentation. This is applied to two challenging problems, i.e. the prediction of extreme wave height and fog prediction. On the one hand, the number of extreme values in SWH time series is less with respect to the number of standard values. In this way, the prediction of these values cannot be done using standard algorithms without taking into account the imbalanced ratio of the dataset. For that, an algorithm that automatically finds the set of segments and then applies EANNs is developed, showing the high ability of the algorithm to detect and predict these special events. On the other hand, fog prediction is affected by the same problem, that is, the number of fog events is much lower tan that of non-fog events, requiring a special treatment too. A preprocessing of different data coming from sensors situated in different parts of the Valladolid airport are used for making a simple ANN model, which is physically corroborated and discussed. The last challenge which opens new horizons is the estimation of the statistical distribution of time series to guide different methodologies. For this, the estimation of a mixed distribution for SWH time series is then used for fixing the threshold of POT approaches. Also, the determination of the fittest distribution for the time series is used for discretising it and making a prediction which treats the problem as ordinal classification. The work developed in this Thesis is supported by twelve papers in international journals, seven papers in international conferences, and four papers in national conferences

COSCO: container orchestration using co-simulation and gradient based optimization for fog computing environments

Intelligent task placement and management of tasks in large-scale fog platforms is challenging due to the highly volatile nature of modern workload applications and sensitive user requirements of low energy consumption and response time. Container orchestration platforms have emerged to alleviate this problem with prior art either using heuristics to quickly reach scheduling decisions or AI driven methods like reinforcement learning and evolutionary approaches to adapt to dynamic scenarios. The former often fail to quickly adapt in highly dynamic environments, whereas the latter have run-times that are slow enough to negatively impact response time. Therefore, there is a need for scheduling policies that are both reactive to work efficiently in volatile environments and have low scheduling overheads. To achieve this, we propose a Gradient Based Optimization Strategy using Back-propagation of gradients with respect to Input (GOBI). Further, we leverage the accuracy of predictive digital-twin models and simulation capabilities by developing a Coupled Simulation and Container Orchestration Framework (COSCO). Using this, we create a hybrid simulation driven decision approach, GOBI*, to optimize Quality of Service (QoS) parameters. Co-simulation and the back-propagation approaches allow these methods to adapt quickly in volatile environments. Experiments conducted using real-world data on fog applications using the GOBI and GOBI* methods, show a significant improvement in terms of energy consumption, response time, Service Level Objective and scheduling time by up to 15, 40, 4, and 82 percent respectively when compared to the state-of-the-art algorithms

MSC: A Dataset for Macro-Management in StarCraft II

Macro-management is an important problem in StarCraft, which has been studied for a long time. Various datasets together with assorted methods have been proposed in the last few years. But these datasets have some defects for boosting the academic and industrial research: 1) There're neither standard preprocessing, parsing and feature extraction procedures nor predefined training, validation and test set in some datasets. 2) Some datasets are only specified for certain tasks in macro-management. 3) Some datasets are either too small or don't have enough labeled data for modern machine learning algorithms such as deep neural networks. So most previous methods are trained with various features, evaluated on different test sets from the same or different datasets, making it difficult to be compared directly. To boost the research of macro-management in StarCraft, we release a new dataset MSC based on the platform SC2LE. MSC consists of well-designed feature vectors, pre-defined high-level actions and final result of each match. We also split MSC into training, validation and test set for the convenience of evaluation and comparison. Besides the dataset, we propose a baseline model and present initial baseline results for global state evaluation and build order prediction, which are two of the key tasks in macro-management. Various downstream tasks and analyses of the dataset are also described for the sake of research on macro-management in StarCraft II. Homepage: https://github.com/wuhuikai/MSC.Comment: Homepage: https://github.com/wuhuikai/MS