Distributed MPC for Thermal Comfort and Load Allocation with Energy Auction

This paper presents a distributed predictive control methodology for indoor thermal comfort that optimizes the consumption of a limited shared energy resource using an integrated demand-side management approach that involves a power price auction and an appliance loads allocation scheme. The control objective for each subsystem (house or building) aims to minimize the energy cost while maintaining the indoor temperature inside comfort limits. In a distributed coordinated multi-agent ecosystem, each house or building control agent achieves its objectives while sharing, among them, the available energy through the introduction of particular coupling constraints in their underlying optimization problem. Coordination is maintained by a daily green energy auction bring in a demand-side management approach. Also the implemented distributed MPC algorithm is described and validated with simulation studies.

Indoor school environment: easy and low cost to assess inorganic pollutants

Total particulate matter (TPM) was passively collected inside two classrooms of each of five elementary schools in Lisbon, Portugal. TPM was collected in polycarbonate filters with a 47 mm diameter, placed inside of uncovered plastic petri dishes. The sampling period was from 19 May to 22 June 2009 (35 days exposure) and the collected TPM masses varied between 0.2 mg and 0.8 mg. The major elements were Ca, Fe, Na, K, and Zn at μg level, while others were at ng level. Pearson′s correlation coefficients above 0.75 (a high degree of correlation) were found between several elements. Soil-related, traffic soil re-suspension and anthropogenic emission sources could be identified. Blackboard chalk was also identified through Ca large presence. Some of the determined chemical elements are potential carcinogenic. Quality control of the results showed good agreement as confirmed by the application of u-score test.

Partial utility-driven scheduling for flexible SLA and pricing arbitration in clouds

Cloud SLAs compensate customers with credits when average availability drops below certain levels. This is too inflexible because consumers lose non-measurable amounts of performance being only compensated later, in next charging cycles. We propose to schedule virtual machines (VMs), driven by range-based non-linear reductions of utility, different for classes of users and across different ranges of resource allocations: partial utility. This customer-defined metric, allows providers transferring resources between VMs in meaningful and economically efficient ways. We define a comprehensive cost model incorporating partial utility given by clients to a certain level of degradation, when VMs are allocated in overcommitted environments (Public, Private, Community Clouds). CloudSim was extended to support our scheduling model. Several simulation scenarios with synthetic and real workloads are presented, using datacenters with different dimensions regarding the number of servers and computational capacity. We show the partial utility-driven driven scheduling allows more VMs to be allocated. It brings benefits to providers, regarding revenue and resource utilization, allowing for more revenue per resource allocated and scaling well with the size of datacenters when comparing with an utility-oblivious redistribution of resources. Regarding clients, their workloads’ execution time is also improved, by incorporating an SLA-based redistribution of their VM’s computational power.

Low-cost vibration sensors: tendencies and applications in condition monitoring of machines and structures

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Mecânica