Nearest Neighbors-Based Forecasting for Electricity Demand Time Series in Streaming

This paper presents a new forecasting algorithm for time series in streaming named StreamWNN. The methodology has two well-differentiated stages: the algorithm searches for the nearest neighbors to generate an initial prediction model in the batch phase. Then, an online phase is carried out when the time series arrives in streaming. In par-ticular, the nearest neighbor of the streaming data from the training set is computed and the nearest neighbors, previously computed in the batch phase, of this nearest neighbor are used to obtain the predictions. Results using the electricity consumption time series are reported, show-ing a remarkable performance of the proposed algorithm in terms of fore-casting errors when compared to a nearest neighbors-based benchmark algorithm. The running times for the predictions are also remarkableMinisterio de Ciencia, Innovación y Universidades TIN2017-88209-C

CPS Data Streams Analytics based on Machine Learning for Cloud and Fog Computing: A Survey

Cloud and Fog computing has emerged as a promising paradigm for the Internet of things (IoT) and cyber-physical systems (CPS). One characteristic of CPS is the reciprocal feedback loops between physical processes and cyber elements (computation, software and networking), which implies that data stream analytics is one of the core components of CPS. The reasons for this are: (i) it extracts the insights and the knowledge from the data streams generated by various sensors and other monitoring components embedded in the physical systems; (ii) it supports informed decision making; (iii) it enables feedback from the physical processes to the cyber counterparts; (iv) it eventually facilitates the integration of cyber and physical systems. There have been many successful applications of data streams analytics, powered by machine learning techniques, to CPS systems. Thus, it is necessary to have a survey on the particularities of the application of machine learning techniques to the CPS domain. In particular, we explore how machine learning methods should be deployed and integrated in cloud and fog architectures for better fulfilment of the requirements, e.g. mission criticality and time criticality, arising in CPS domains. To the best of our knowledge, this paper is the first to systematically study machine learning techniques for CPS data stream analytics from various perspectives, especially from a perspective that leads to the discussion and guidance of how the CPS machine learning methods should be deployed in a cloud and fog architecture

Energy Consumption Prediction Using Data Reduction and Ensemble Learning Techniques

Building energy problems have various kinds of aspects, one of which is the difficulty of measuring energy efficiency. With current data development, energy efficiency measurements can be made by developing predictive models to estimate future building needs. However, with the massive amount of data, several problems arise regarding data quality and the lack of scalability in terms of computation memory and time in modeling. In this study, we used data reduction and ensemble learning techniques to overcome these problems. We used numerosity reduction, dimension reduction, and a LightGBM model based on boosting added with a bagging technique, which we compared with incremental learning. Our experimental results showed that the numerosity reduction and dimension reduction techniques could speed up the training process and model prediction without reducing the accuracy. Testing the ensemble learning model also revealed that bagging had the best performance in terms of RMSE and speed, with an RMSE of 262.304 and 1.67 times faster than the model with incremental learning