Prediction of home energy consumption based on gradient boosting regression tree

Abstract Energy consumption prediction of buildings has drawn attention in the related literature since it is very complex and affected by various factors. Hence, a challenging work is accurately estimating the energy consumption of buildings and improving its efficiency. Therefore, effective energy management and energy consumption forecasting are now becoming very important in advocating energy conservation. Many researchers work on saving energy and increasing the utilization rate of energy. Prior works about the energy consumption prediction combine software and hardware to provide reasonable suggestions for users based on the analyzed results. In this paper, an innovative energy consumption prediction model is established to simulate and predict the electrical energy consumption of buildings. In the proposed model, the energy consumption data is more accurately predicted by using the gradient boosting regression tree algorithm. By comparing the performance index Root Mean Square Error of different prediction models through experiments it is shown that the proposed model obtains lower values on different testing data. More detailed comparison with other existing models through experiments show that the proposed prediction model is superior to other models in energy consumption prediction

Reduced-Complexity Verification for K-Step and Infinite-Step Opacity in Discrete Event Systems

Opacity is a property that captures security concerns in cyber-physical systems and its verification plays a significant role. This paper investigates the verifications of K-step and infinite-step weak and strong opacity for partially observed nondeterministic finite state automata. K-step weak opacity is checked by constructing, for some states in the observer, appropriate state-trees, to propose a necessary and sufficient condition. Based on the relation between K-step weak and infinite-step weak opacity, a condition that determines when a system is not infinite-step weak opaque is presented. Regarding K-step and infinite-step strong opacity, we develop a secret-involved projected automaton, based on which we construct secret-unvisited state trees to derive a necessary and sufficient condition for K-step strong opacity. Furthermore, an algorithm is reported to compute a verifier that can be used to obtain a necessary and sufficient condition for infinite-step strong opacity. It is argued that, in some particular cases, the proposed methods achieve reduced complexity compared with the state of the art

Sliced Wasserstein Generative Models

In generative modeling, the Wasserstein distance (WD) has emerged as a useful metric to measure the discrepancy between generated and real data distributions. Unfortunately, it is challenging to approximate the WD of high-dimensional distributions. In contrast, the sliced Wasserstein distance (SWD) factorizes high-dimensional distributions into their multiple one-dimensional marginal distributions and is thus easier to approximate. In this paper, we introduce novel approximations of the primal and dual SWD. Instead of using a large number of random projections, as it is done by conventional SWD approximation methods, we propose to approximate SWDs with a small number of parameterized orthogonal projections in an end-to-end deep learning fashion. As concrete applications of our SWD approximations, we design two types of differentiable SWD blocks to equip modern generative frameworks---Auto-Encoders (AE) and Generative Adversarial Networks (GAN). In the experiments, we not only show the superiority of the proposed generative models on standard image synthesis benchmarks, but also demonstrate the state-of-the-art performance on challenging high resolution image and video generation in an unsupervised manner.Comment: This paper is accepted by CVPR 2019, accidentally uploaded as a new submission (arXiv:1904.05408, which has been withdrawn). The code is available at this https URL https:// github.com/musikisomorphie/swd.gi

Selection of a stealthy and harmful attack function in discrete event systems

In this paper we consider the problem of joint state estimation under attack in partially-observed discrete event systems. An operator observes the evolution of the plant to evaluate its current states. The attacker may tamper with the sensor readings received by the operator inserting dummy events or erasing real events that have occurred in the plant with the goal of preventing the operator from computing the correct state estimation. An attack function is said to be harmful if the state estimation consistent with the correct observation and the state estimation consistent with the corrupted observation satisfy a given misleading relation. On the basis of an automaton called joint estimator, we show how to compute a supremal stealthy joint subestimator that allows the attacker to remain stealthy, no matter what the future evolution of the plant is. Finally, we show how to select a stealthy and harmful attack function based on such a subestimator