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

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

Variation of the network (MAP estimator) test MSE with the number of hidden neuron for four training algorithms.

<p>Variation of the network (MAP estimator) test MSE with the number of hidden neuron for four training algorithms.</p