The Equivalent Thermal Parameter Model and Simulation of Air Conditioner System in Demand Response Programs

AbstractEstimating end-use energy consumption that accurately reflects the variance of the end-load is critical for the grid wise simulation and analysis work. In a house, the largest load with a thermal cycle is often the heating ventilation and air conditioning (HVAC) system. So the thermal dynamics of typical residential electric air conditioner is discussed, and then an equivalent thermal parameters (ETP) model is built by the thermal equilibrium in this paper. Based on this, the switch status, indoor air temperature and power consumption are simulated through control strategies of constant thermostat set point. The results show according the switch status, indoor air temperature can be calculated by the ETP model, thus give the desire status to the grid according the setting temperature. In summer, with the increasing of setting temperature, the frequency of on-off becomes lower, thus the power consumption also reduces from 1200kW (26°C) to 970kW (27°C). So if some control strategies are used, the model will play an important part on decreasing the peak-average rate of the power grid and also improving the load rate of grid

Eccentricity-paced monsoon variability on the northeastern Tibetan Plateau in the Late Oligocene high CO 2 world

Constraining monsoon variability and dynamics in the warm unipolar icehouse world of the Late Oligocene can provide important clues to future climate responses to global warming. Here, we present a ~4-thousand year (ka) resolution rubidium-to-strontium ratio and magnetic susceptibility records between 28.1 and 24.1 million years ago from a distal alluvial sedimentary sequence in the Lanzhou Basin (China) on the northeastern Tibetan Plateau margin. These Asian monsoon precipitation records exhibit prominent short (~110-ka) and long (405-ka) eccentricity cycles throughout the Late Oligocene, with a weak expression of obliquity (41-ka) and precession (19-ka and 23-ka) cycles. We conclude that a combination of eccentricity-modulated low-latitude summer insolation and glacial-interglacial Antarctic Ice Sheet fluctuations drove the eccentricity-paced precipitation variability on the northeastern Tibetan Plateau in the Late Oligocene high CO2 world by governing regional temperatures, water vapor loading in the western Pacific and Indian Oceans, and the Asian monsoon intensity and displacement

Characterizing Database User’s Access Patterns

Abstract. Much work has been done on characterizing the workload of a database system. Previous studies focused on providing different types of statistical summaries, and modeling the run-time behavior on the physical resource level as well. In this paper, we focus on characterizing the database system’s workload from the view of database user. We use user access patterns to describe how a client application or a group of users access the data of a database system. The user access patterns include a set of user access events that represent the format of the queries and a set of user access graphs that represent the query execution orders. User access patterns can help database administrators tune the system, help database users optimize queries, and help to predict and cache future queries. In this paper, we will present several approaches to use user access patterns to improve system performance, and report some experimental results.

Applying Language Modeling to Session Identification from Database Trace Logs

A database session is a sequence of requests presented to the database system by a user or an application to achieve a certain task. Session identification is an important step in discovering useful patterns from database trace logs. The discovered patterns can be used to improve the performance of database systems by prefetching predicted queries, rewriting the current query or conducting effective cache replacement. In this paper, we present an application of a new session identification method based on statistical language modeling to database trace logs. Several problems of the language modeling based method are revealed in the application, which include how to select values for the parameters of the language model, how to evaluate the accuracy of the session identification result and how to learn a language model without well-separated training data. All of these issues are important in the successful application of the language modeling based method for session identification. We propose solutions to these open issues. In particular, new methods for determining an entropy threshold and the order of the language model are proposed. New performance measures are presented to better evaluate the accuracy of the identified sessions. Furthermore, three types of learning methods, namely, supervised, semi-supervised and unsupervised learning, are introduced to lear

Image Retrieval using the Improved Double Density Contourlet Transform

A new image retrieval algorithm based on the improved double density contourlet transform(DDCT) is proposed in this paper. The improved double density contourlet transform have the property of shift invariance and non-Gaussian for the high frequency sub-bands. In order to obtain the high frequency texture feature, we design the quantization histograms using the high frequency direction sub-bands. The 0/1 quantization is used to deal with the energy matrix of high frequency sub-bands. A texture point is defined as an 8-D vector by integrating different channel values from high frequency sub-bands and the quantization histograms are computed. In order to get the texture-spatial features, the local binary pattern is used to describe the texture feature of low frequency sub-band. Then the quantization histograms and the local binary pattern (LBP) can be used to denote the texture features of the image. Experiments show that the proposed algorithm using the improved double density contourlet transform outperforms the SD algorithm based on the contourlet transform in the natural image retrieval