Yoshiki Makino

Development of a smart community simulator with individual emulation modules for community facilities and houses

A smart community simulator that simulates electricity consumption and thermal energy consumption is being developed by the authors. The simulator is designed to be able to work with modules implemented as individual external emulators of many kinds of facilities and homes. With this simulator, we can change aspects and features of the community and estimate the long-term effects of these changes on the community as a whole. In this paper we introduced the design and implementation of the simulator.

Building High-Accuracy Thermal Simulation for Evaluation of Thermal Comfort in Real Houses

Thermal comfort is an essential aspect for the control and verification of many smart home services. In this research, we design and implement simulation which models thermal environment of a smart house testbed. Our simulation can be used to evaluate thermal comfort in various conditions of home environment. In order to increase the accuracy of the simulation, we measure thermal-related parameters of the house such as temperature, humidity, solar radiation by the use of sensors and perform parameter identification to estimate uncertain parameters in our thermal model. We also implement a communication interface which allows our simulator to communicate with other external simulators. Experimental result showed that our simulation can achieve high accuracy when compared with actual measurement data.

Emulation-Based ICT System Resiliency Verification for Disaster Situations

Disaster situations require resilient ICT systems in order to provide as good as possible communication conditions in such catastrophic circumstances. The resiliency verification of ICT systems is however difficult, because reproducing large-scale disaster conditions in production networks is impossible without affecting their users. In this paper we present an emulation-based approach for evaluating the resiliency of ICT systems in disaster situations. This is achieved by reproducing disaster-like conditions in a emulated environment running on a large-scale test bed on which actual network protocols and applications are executed in real time. The experimental results presented in the paper demonstrate how the effects of both the emulated disasters and those of the recovery technologies we subsequently deploy can be quantified objectively. This methodology can be used to improve the resilience of the systems under test.

Architecture for Organizing Context-Aware Data in Smart Home for Activity Recognition System

Knowing human activity in each day is relevant information in several purposes. However, existing activity recognition systems have limitation to identify the human activity because they cannot get the appropriate information for recognition. To address this limitation, we present three relevant components in Context-aware Activity Recognition Engine CARE architecture for organizing context-aware information in home. First, we introduce Context Sensor Network CSN. The CSN provides the raw environment information from the diversity of sensors. Second, data manager component is proposed to process the pre-processing in the raw data from the CSN. The data must be normalized and transformed in order to make the system more efficient. The last component is system repository that composes of three essential tasks for controlling the information in the system. In this paper, the ontology based activity recognition OBAR system is used to evaluate the data from proposed components. The high accuracy of results can refer to the well organization of proposed components.

Towards a home environment testbed: Experiments with human body simulators and a real house

This paper illustrates a design and implementation of a home environment testbed. The proposed testbed aims to support experiments on real home environments with human subjects. However, conducting experiments with real human subjects has several limitations regarding costs, time, and user privacy. In order to avoid such issues, human body simulators (HBSims), which are designed to support 24-hour in-house repeatable experiments, are employed. Using HBSims, the patterns of human humidity and temperature are generated. Based on human activity schedules together with home appliance usage time obtained from one family, HBSims were employed in experiments in a real house. The evaluation results demonstrate that (1) our proposed testbed can potentially be used for real home environment experiments and (2) HBSims can be used instead of real human subjects for studying the effects of a human body in real home environments.

Simplifying the Auto Regressive and Moving Average (ARMA) Model Representing the Dynamic Thermal Behaviour of iHouse Based on Theoretical Knowledge

Modelling and simulation is an alternative way of testing the dynamic behaviour of a real system – in some situation, testing the real system are expensive, time consuming, not comfortable, and dangerous. Mathematical model describing the dynamic behaviour of a system can be represented by using white, black, or grey box model. This study focuses on developing a simplified Auto Regressive Moving Average (ARMA) model (a type of linear black model) to represent the dynamic thermal behaviour of iHouse – simplification is done based on the theoretical knowledge of the building. The performance of the simplified ARMA model developed in this study is compared with the performance of the models developed in previous studies, which are: (1) House Thermal Simulator; (2) and ARMA model. Result shows that the simplified ARMA model developed in this study consists of simpler set of mathematical equations, but can still simulate the dynamic thermal behaviour of iHouse with the accuracy that is almost on par with the models developed in previous studies.

Short-term prediction of energy consumption of air conditioners based on weather forecast

In residential houses, air conditioners consume a lot of electrical energy. In order to improve energy efficiency for residential houses, short-term prediction of energy consumption of air conditioners is required. In this paper, we propose the use of our thermal simulation to simulate the change of room temperature based on weather forecast information and predict the energy consumption of an air conditioner in a residential house. In order to calculate solar radiation heat flux, which contributes a lot to the change of room temperature, we utilize a neural network model to predict global solar radiation using training data obtained from weather stations. We also utilize a PID control model to simulate the operation of air conditioners. The accuracy of our simulation is verified by experiments carried out at a real testbed house.