Azkario Rizky Pratama

WSAN-based energy efficient system in building: A monitoring and scheduling

In response to environmental issues about energy saving and reducing CO2 emissions, energy consumption monitoring is desirable particularly in office or school premise. In this paper, we propose a system to monitor and control electrical devices in a building. The system consists of pervasive layer, controller, and application layer. Wireless Sensor Network is utilized to actuate the appliances and sense the electricity usage. The system was deployed and analyzed in a Universitys test-bed. To measure the system performance, comparison between with and without scheduling state of electrical devices is performed. The result shows energy efficiency of 41.79%.

Comparison of energy consumption in Wi-Fi and bluetooth communication in a Smart Building

Modern Smart Buildings will utilize sensor technologies to obtain current information of the occupants and use it to provide automatic services to improve the safety, efficient use, productivity, and comfort. Energy saving is one of the promises of Smart Buildings. This can be achieved by controlling the HVAC system, lights, and other energy consuming appliances in relation to the activities of people in the building. In particular, presence is an important factor to consider. By using beacons such as those based on Bluetooth Low Energy (BLE) and mobile phones, one can deduct occupancy information by interacting, over WiFi, with a central server. In the present study, we investigate BLE utilization for transmitting the occupancy data to the server using off-the-self devices with several parameters, e.g., distance, packet size, and line-of-sight state. Furthermore, we propose a utilization of size-constrained BLE packets for transmitting occupancy data, which was previously only possible by means of HTTP over WiFi. The result shows that it is possible to transmit occupancy data via BLE, despite of its resource constraints. Bluetooth is also shown to be about 30% more energy efficient than WiFi to perform occupancy data transmission, i.e., the mobile phone has 16 hours and 38 minutes of battery life when running on BLE communication scheme and 14 hours and 46 minutes in WiFi communication.

An infrastructure-less occupant context-recognition in energy efficient building

Energy efficient building refers to building with an integrated system that is aware toward energy usage and has an effort to automatically reduce all electrical energy wasting by turning some unused devices off. In order to decide an appropriate decision, such a system needs surrounding contexts, such as location and activity that are obtained from sensors around the environment. We propose a novel infrastructure-less occupant context recognition, where we only need off-the-shelf sensors in a mobile device to recognize room occupancy. The idea is to derive the occupant context from user position gathered by real time Dead Reckoning (DR) technique on a mobile phone. Moreover, our system is able to control electrical devices based on the occupant context in certain room. Our experiments in a laboratory environment show that it can be really implemented and prospectively replacing Passive Infrared (PIR) sensors in occupancy detection due to smaller delay compared to 10 seconds timeout which is commonly determined by many energy-efficient systems based on PIR-sensor to reach the unoccupied state.

Mining Sequential Patterns for Appliance Usage Prediction

Reducing the energy consumption in buildings and homes can be achieved by predicting how energy-consuming appliances are used, and by discovering their patterns. To mine these patterns, a smart-metering architecture needs to be in place complemented by appropriate data analysis mechanisms. Once the usage patterns are obtained, they can be employed to optimize the way energy from renewable installations, home batteries, and even micro grids is managed. We present an approach and related experiments for mining sequential patterns in appliance usage. In particular, we mine patterns that allow us to perform device usage prediction, energy usage prediction, and device usage prediction with failed sensors. The focus of this work is on the sequential relationships between the state of distinct devices. We use data sets from three existing buildings, of which two are households and one is an office building. The data is used to train our modified Support-Pruned Markov Models which use a relative support threshold. Our experiments show the viability of the approach, as we achieve an overall accuracy of 87% in device usage predictions, and up to 99% accuracy for devices that have the strongest sequential relationships. For these devices, the energy usage predictions have an accuracy of around 90%. Predicting device usage with failed sensors is feasible, assuming there is a strong sequential relationship for the devices.

Multi-User Low Intrusive Occupancy Detection

Smart spaces are those that are aware of their state and can act accordingly. Among the central elements of such a state is the presence of humans and their number. For a smart office building, such information can be used for saving energy and safety purposes. While acquiring presence information is crucial, using sensing techniques that are highly intrusive, such as cameras, is often not acceptable for the building occupants. In this paper, we illustrate a proposal for occupancy detection which is low intrusive; it is based on equipment typically available in modern offices such as room-level power-metering and an app running on workers’ mobile phones. For power metering, we collect the aggregated power consumption and disaggregate the load of each device. For the mobile phone, we use the Received Signal Strength (RSS) of BLE (Bluetooth Low Energy) nodes deployed around workspaces to localize the phone in a room. We test the system in our offices. The experiments show that sensor fusion of the two sensing modalities gives 87–90% accuracy, demonstrating the effectiveness of the proposed approach.

Power-Based Device Recognition for Occupancy Detection

Each person using electrical devices leaves electricity fingerprints in the form of power consumption. These can be very useful for understanding the context of that person in, for instance, a smart office. A device that is highly correlated with the presence of a person in an office is the computer monitor; the correlation is in the range 83–96%. Therefore, it is useful to recognize from an aggregated power load the portion that is due to computer monitors. In this paper, we propose an event-based device recognition approach. After studying several predictors and features for device classification, we build a prototype for the classification. We evaluate the approach with actual power measurement of seven office monitors used by four workers in an office environment. Our experiments show that the approach is feasible and the per-day accuracy ranges in the range 69–80% for seven and five physical devices, respectively.