Adaptive approach in handling human inactivity in computer power management

Human inactivity is handled by adapting the behavioral changes of the users.Human inactivity refers to as unpredictable workload of a complex system that is caused by increments of amount in power consumption and it can be handled automatically without the need to set a fixed time for changing the computer state.This is happens due to lack of knowledge in a software system and the software self-adaptation is one approach in dealing with this source of uncertainty. This paper observes human inactivity and Power management policy through the application of reinforcement learning approach in the computer usage and finds that computer power usage can be reduced if the idle period can be intelligently sensed from the user activities. This study introduces Control, Learn and Knowledge model that adapts the Monitor, Analyze, Planning, Execute control loop integrates with Q Learning algorithm to learn human inactivity period to minimize the computer power consumption.An experiment to evaluate this model was conducted using three case studies with same activities. The result show that the proposed model obtained those 5 out of 12 activities shows the power decreasing compared to other

Adaptive Approach in Handling Human Inactivity in Computer Power Management

Human inactivity is handled by adapting the behavioral changes of the users. Human inactivity refers to as unpredictable workload of a complex system that is caused by increments of amount in power consumption and it can be handled automatically without the need to set a fixed time for changing the computer state. This is happens due to lack of knowledge in a software system and the software self-adaptation is one approach in dealing with this source of uncertainty. This paper observes human inactivity and Power management policy through the application of reinforcement learning approach in the computer usage and finds that computer power usage can be reduced if the idle period can be intelligently sensed from the user activities. This study introduces Control, Learn and Knowledge model that adapts the Monitor, Analyze, Planning, Execute control loop integrates with Q Learning algorithm to learn human inactivity period to minimize the computer power consumption. An experiment to evaluate this model was conducted using three case studies with same activities. The result show that the proposed model obtained those 5 out of 12 activities shows the power decreasing compared to other

Advanced Occupancy Measurement Using Sensor Fusion

With roughly about half of the energy used in buildings attributed to Heating, Ventilation, and Air conditioning (HVAC) systems, there is clearly great potential for energy saving through improved building operations. Accurate knowledge of localised and real-time occupancy numbers can have compelling control applications for HVAC systems. However, existing technologies applied for building occupancy measurements are limited, such that a precise and reliable occupant count is difficult to obtain. For example, passive infrared (PIR) sensors commonly used for occupancy sensing in lighting control applications cannot differentiate between occupants grouped together, video sensing is often limited by privacy concerns, atmospheric gas sensors (such as CO2 sensors) may be affected by the presence of electromagnetic (EMI) interference, and may not show clear links between occupancy and sensor values. Past studies have indicated the need for a heterogeneous multi-sensory fusion approach for occupancy detection to address the short-comings of existing occupancy detection systems. The aim of this research is to develop an advanced instrumentation strategy to monitor occupancy levels in non-domestic buildings, whilst facilitating the lowering of energy use and also maintaining an acceptable indoor climate. Accordingly, a novel multi-sensor based approach for occupancy detection in open-plan office spaces is proposed. The approach combined information from various low-cost and non-intrusive indoor environmental sensors, with the aim to merge advantages of various sensors, whilst minimising their weaknesses. The proposed approach offered the potential for explicit information indicating occupancy levels to be captured. The proposed occupancy monitoring strategy has two main components; hardware system implementation and data processing. The hardware system implementation included a custom made sound sensor and refinement of CO2 sensors for EMI mitigation. Two test beds were designed and implemented for supporting the research studies, including proof-of-concept, and experimental studies. Data processing was carried out in several stages with the ultimate goal being to detect occupancy levels. Firstly, interested features were extracted from all sensory data collected, and then a symmetrical uncertainty analysis was applied to determine the predictive strength of individual sensor features. Thirdly, a candidate features subset was determined using a genetic based search. Finally, a back-propagation neural network model was adopted to fuse candidate multi-sensory features for estimation of occupancy levels. Several test cases were implemented to demonstrate and evaluate the effectiveness and feasibility of the proposed occupancy detection approach. Results have shown the potential of the proposed heterogeneous multi-sensor fusion based approach as an advanced strategy for the development of reliable occupancy detection systems in open-plan office buildings, which can be capable of facilitating improved control of building services. In summary, the proposed approach has the potential to: (1) Detect occupancy levels with an accuracy reaching 84.59% during occupied instances (2) capable of maintaining average occupancy detection accuracy of 61.01%, in the event of sensor failure or drop-off (such as CO2 sensors drop-off), (3) capable of utilising just sound and motion sensors for occupancy levels monitoring in a naturally ventilated space, (4) capable of facilitating potential daily energy savings reaching 53%, if implemented for occupancy-driven ventilation control