Energy consumption prediction through linear and non-linear baseline energy model

This paper presents a design, simulation and performance evaluation of an optimized model for the Heating, Ventilation and Air-Conditioning (HVAC) systems using intelligent control algorithm. Fanger’s comfort method and genetic algorithms were used to obtain the optimal and initial values. The heat transmission coefficient between internal and external environments were determined depending on several inputs and factors acquired via supervisory control and data acquisition (SCADA) system sensors. The main feature of the real-time model is the prediction of the internal buildings environment, in order to control HVAC system for indoor environment and to utilize the optimum power consumed depending on optimized air temperature value. The predicted air temperature value and Predictive Mean Vote (PMV) value was applied using intelligent algorithm to obtain an optimal comfort level of the air temperature. The optimized air temperature value can be used in HVAC system controller to ensure that the temperature of indoor can reach a specific value after a known period of time. The use of genetic algorithm (GE) ensures that the used power is well below its peak value and maintains the comfort of the user’s environment.

Section: Design Of Typical Hvac/scada System Application Of Ibmsmentioning

confidence: 99%

Optimization of energy consumption and thermal comfort for intelligent building management system using genetic algorithm

Mohammed¹,

Awad²,

Radhi³

2020

“…In order to calculate the coulombic efficiency, the internal resistance of the Pandanus Amaryllifolius was calculated using ohm's law, R=V/I [32]. Assuming the acetate was completely utilized to generate electricity using (15), Coulombic efficiency (CE%) was then calculated as (17), where Ex C are the total coulombs calculated with the integration of current measured over a time interval ( i ), and Th C is the theoretical amount of coulomb from acetate.…”

Section: Electrical Energy Conversion Efficiencymentioning

confidence: 99%

Electrical energy production from plant biomass: an analysis model development for Pandanus Amaryllifolius plant microbial fuel cell

Cheng

Ching

Uttraphan

et al. 2020

Plant microbial fuel cell (P-MFC) is an electrochemical reactor that converts organic compounds to electrical energy through the catalytic reaction from electrochemically active bacteria (EAB). However, there is no sign of an attempt in developing the functional model in predicting the energy conversion and utilization of P-MFC. In this study, an analytic model is proposed to show the whole production process of the organic compound to electrical energy generation. Pandanus Amaryllifolius plant was used as sources of photosynthate, where biomass product from rhizodeposition, acetate was produced, and soil bacteria as the microbial culture, and air as the input to the cathode chamber. The proposed analytical model is able to predict the output of the P-MFC using the parameters from the experiment. The generated data from the model was then compared with the monitored data from the Pandanus Amaryllifolius P-MFC. The results show the electrical power output has a high similarity pattern with the bacterial growth curve model and able to achieve the coulombic efficiency of 95.32%.

“…Multi-layer neural networks have been widely used to estimate the nonlinear relationship between input and output variables with a certain level of accuracy [21][22][23]. Recently, neural networks have been successfully used to identify and estimate the heat load and control air conditioning systems [24][25][26][27].…”

Section: Use Of Neural Network In Calculation Of Thermal Comfort Coefficientmentioning

confidence: 99%

Thermal comfort control via air conditioning system using fuzzy neural network feedback controller

Mohamadi

Ahmed

2020

Despite their complexity and uncertainty, air conditioning systems should provide the optimal thermal conditions in a building. These controller systems should be adaptable to changes in environmental parameters. In most air conditioning systems, today, there are On/Off controllers or PID in more advanced types, which, due to different environmental conditions, are not optimal and cannot provide the optimal environmental conditions. Controlling thermal comfort of an air conditioning system requires estimation of thermal comfort index. In this study, fuzzy controller was used to provide thermal comfort in an air conditioning system, and neural network was used to estimate thermal comfort in the feedback path of the controller. Fuzzy controller has a good response given the non-linear features of air conditioning systems. In addition, the neural network makes it possible to use thermal comfort feedback in a real-time control.