Optimized scheduling for an airconditioning system based on indoor thermal comfort using the multiobjective improved global particle swarm optimization

In energy management system (EMS), the scheduling of air-conditioning (AC) system has been shown to reduce considerable amount of its power consumption with relatively low implementation cost. However, most scheduling methods lack a systematic approach to ensuring optimal power consumption reduction and comfort experienced by occupants. The main contribution of this paper is a new optimized AC scheduling approach that focuses on indoor thermal comfort using a new multi-objective optimization algorithm, called the improved global particle swarm optimization (IGPSO), which able to find better optimal solutions faster than its original version, the global particle swarm optimization (GPSO) algorithm. IGPSO is used to model the building characteristics and to find optimum indoor temperature values for the room/building. The proposed technique is based on predicted mean vote (PMV) comfort index that is able to reduce AC power consumption while maintaining indoor comfort throughout its operation. The schedule is set in advance by making use of weather forecast and the estimation of building characteristic parameters. This technique can be implemented on existing buildings with existing HVAC systems with minimal modifications to the HVAC infrastructure. Experimental results show that the proposed method is able to provide good PMV while consuming less power compared to the commonly used extended pre-cooling technique

Design of a robust active fuzzy parallel distributed compensation anti-vibration controller for a hand-glove system

Undesirable vibrations resulting from the use of vibrating hand-held tools decrease the tool performance and user productivity. In addition, prolonged exposure to the vibration can cause ergonomic injuries known as the hand-arm vibration syndrome (HVAS). Therefore, it is very important to design a vibration suppression mechanism that can isolate or suppress the vibration transmission to the users’ hands to protect them from HAVS. While viscoelastic materials in anti-vibration gloves are used as the passive control approach, an active vibration control has shown to be more effective but requires the use of sensors, actuators and controllers. In this paper, the design of a controller for an anti-vibration glove is presented. The aim is to keep the level of vibrations transferred from the tool to the hands within a healthy zone. The paper also describes the formulation of the hand-glove system’s mathematical model and the design of a fuzzy parallel distributed compensation (PDC) controller that can cater for different hand masses. The performances of the proposed controller are evaluated through simulations and the results are benchmarked with two other active vibration control techniques-proportional integral derivative (PID) controller and active force controller (AFC). The simulation results show a superior performance of the proposed controller over the benchmark controllers. The designed PDC controller is able to suppress the vibration transferred to the user’s hand 93% and 85% better than the PID controller and the AFC, respectively

An improved global particle swarm optimization for faster optimization process

An efficient Global Particle Swarm Optimization (GPSO) is proposed in order to overcome the concern of trapping in the local optimal point especially in high dimensional while using ordinary Particle Swarm Optimization (PSO). GPSO is able to bring all the particles to be closely clumped together faster than PSO. In this paper, an improved GPSO is proposed in order to get a closely clumped particles group faster than using GPSO. The original GPSO is improved by taking into account the global best fitness error and particle fitness clumping size of every iteration. The improved GPSO is simulated by using several two dimension mathematical function and benchmarked with the original GPSO. The improved GPSO is shown to be able to obtain closely clumped particles much more faster than the original GPSO up to 62%. The performances are also evaluated by comparing the standard deviation, average, best particle and worst particles obtained through a 50 independent runs. In term of the four factors mentioned, the improved GPSO performance is shown to be as good of the original GPSO

Implementation of a Scenario-based MPC for HVAC Systems: an Experimental Case Study

Heating, Ventilation and Air Conditioning (HVAC) systems play a fundamental role in maintaining acceptable thermal comfort and air quality levels. Model Predictive Control (MPC) techniques are known to bring significant energy savings potential. Developing effective MPC-based control strategies for HVAC systems is nontrivial since buildings dynamics are nonlinear and influenced by various uncertainties. This complicates the use of MPC techniques in practice. We propose to address this issue by designing a stochastic MPC strategy that dynamically learns the statistics of the building occupancy patterns and weather conditions. The main advantage of this method is the absence of a-priori assumptions on the distributions of the uncertain variables, and that it can be applied to any type of building. We investigate the practical implementation of the proposed MPC controller on a student laboratory, showing its effectiveness and computational tractability.Godkänd; 2014; 20141009 (damvar

Manifold absolute pressure estimation using neural network with hybrid training algorithm

<div><p>In a modern small gasoline engine fuel injection system, the load of the engine is estimated based on the measurement of the manifold absolute pressure (MAP) sensor, which took place in the intake manifold. This paper present a more economical approach on estimating the MAP by using only the measurements of the throttle position and engine speed, resulting in lower implementation cost. The estimation was done via two-stage multilayer feed-forward neural network by combining Levenberg-Marquardt (LM) algorithm, Bayesian Regularization (BR) algorithm and Particle Swarm Optimization (PSO) algorithm. Based on the results found in 20 runs, the second variant of the hybrid algorithm yields a better network performance than the first variant of hybrid algorithm, LM, LM with BR and PSO by estimating the MAP closely to the simulated MAP values. By using a valid experimental training data, the estimator network that trained with the second variant of the hybrid algorithm showed the best performance among other algorithms when used in an actual retrofit fuel injection system (RFIS). The performance of the estimator was also validated in steady-state and transient condition by showing a closer MAP estimation to the actual value.</p></div

The flow chart of LM algorithm.

<p>The flow chart of LM algorithm.</p

Comparison of the MAP estimator output (LM+BR+PSO_a) and actual MAP as function of (a) throttle and (b) speed.

<p>Comparison of the MAP estimator output (LM+BR+PSO_a) and actual MAP as function of (a) throttle and (b) speed.</p

3D Surface plot of the trained estimator (LM+BR+PSO_b).

<p>3D Surface plot of the trained estimator (LM+BR+PSO_b).</p

Comparison between estimated and measured MAP at 1100 rad/s and 80° of throttle angle.

<p>Comparison between estimated and measured MAP at 1100 rad/s and 80° of throttle angle.</p