scholarly journals Physical-Rule Based Adaptive Neuro-Fuzzy Inferential Sensor For Optimal Control Of Space Heating Systems

2021 ◽  
Author(s):  
Liang Huang
Keyword(s):  
Optimal Control ◽  
Prediction Model ◽  
High Energy ◽  
Space Heating ◽  
Indoor Temperature ◽  
Rule Based ◽  
Heating Systems ◽  
Neuro Fuzzy ◽  
Physical Sensors ◽  
Robust Prediction

The previous research on adaptive neuro-fuzzy inferential sensor (ANFIS) presented an approach to estimate the average indoor temperature and proposed a new method to measure process variables which are impossible to measure directly by using physical sensors in buildings. To achieve high energy efficiency in heating systems, an accurate and robust prediction model is essential. This thesis aims to improve the conventional ANFIS indoor temperature estimator and look for an optimal control of space heating systems. A physical-rule based ANFIS prediction model is proposed. The differences between this physical-rule based ANFIS prediction model and the conventional ANFIS prediction model are presented and analyzed. Three performance measures (RMSE, RMS, and R

scholarly journals Physical-Rule Based Adaptive Neuro-Fuzzy Inferential Sensor For Optimal Control Of Space Heating Systems

2021 ◽  
Author(s):  
Liang Huang
Keyword(s):  
Optimal Control ◽  
Prediction Model ◽  
High Energy ◽  
Space Heating ◽  
Indoor Temperature ◽  
Rule Based ◽  
Heating Systems ◽  
Neuro Fuzzy ◽  
Physical Sensors ◽  
Robust Prediction

The previous research on adaptive neuro-fuzzy inferential sensor (ANFIS) presented an approach to estimate the average indoor temperature and proposed a new method to measure process variables which are impossible to measure directly by using physical sensors in buildings. To achieve high energy efficiency in heating systems, an accurate and robust prediction model is essential. This thesis aims to improve the conventional ANFIS indoor temperature estimator and look for an optimal control of space heating systems. A physical-rule based ANFIS prediction model is proposed. The differences between this physical-rule based ANFIS prediction model and the conventional ANFIS prediction model are presented and analyzed. Three performance measures (RMSE, RMS, and R

Physical Rules Based Adaptive Neuro-Fuzzy Inferential Sensor Model Design and Analysis in Predicting the Indoor Temperature in Heating System

2012 ◽  
Vol 516-517 ◽  
pp. 370-379 ◽  
Author(s):  
Liang Huang ◽  
Zai Yi Liao ◽  
Hua Ge ◽  
Lian Zhao
Keyword(s):  
Energy Efficiency ◽  
Prediction Model ◽  
Performance Measures ◽  
Heating System ◽  
Model Design ◽  
Indoor Temperature ◽  
Sensor Model ◽  
Neuro Fuzzy ◽  
Robust Prediction ◽  
Indoor Comfort

The previous research on adaptive neuro-fuzzy inferential systems (ANFIS) presented an approach to estimating the average indoor temperature in the building environment. However, the restriction on robustness limited the energy efficiency and indoor comfort ratio. An accurate and robust prediction model is proposed in this paper. Comparing to the previous unphysical rules based ANFIS prediction model, the improvement of the physical rules based ANFIS prediction model will be presented and the reason of better performance of this new model will be discussed. Three performance measures are using in evaluating the proposed prediction model.

Physical-Rule Based Adaptive Neuro-Fuzzy Inferential Sensor vs. GA-BP Based Prediction Model in Indoor Temperature Predicting

2012 ◽  
Vol 594-597 ◽  
pp. 2179-2185
Author(s):  
Liang Huang ◽  
Zai Yi Liao
Keyword(s):  
Prediction Models ◽  
Back Propagation ◽  
District Heating ◽  
Heating System ◽  
Good Prediction ◽  
Indoor Temperature ◽  
District Heating System ◽  
Rule Based ◽  
Neuro Fuzzy ◽  
Indoor Comfort

The previous research on temperature prediction presented different approaches which are physical-rule based adaptive neuro-fuzzy inferential sensor (ANFIS) model and GA-BP (genetic algorithm back propagation) based model to estimate the average indoor temperature in the building environment. Their good prediction performances improved energy efficiency of district heating system and indoor comfort ratio. However, either of these two models has its drawback in a certain condition. In this paper, the two prediction models are reviewed and evaluated by three performance measures (RMSE, RMS, and R2). Their limitations are discussed and potential solution is proposed.

scholarly journals Potential of Combustion of Poultry Litter for Space Heating in Poultry Production

2019 ◽  
Vol 9 (1) ◽  
pp. 29
Author(s):  
Mogomotsi J. Molefe ◽  
Isaac N. Simate
Keyword(s):  
High Temperatures ◽  
Electricity Generation ◽  
Poultry Litter ◽  
High Energy ◽  
Space Heating ◽  
Energy Costs ◽  
Heat Distribution ◽  
Poultry Production ◽  
Complete Combustion ◽  
Heating Systems

Confined poultry production, which is expected to double by 2050, produces a lot of litter. For successful and sustainable poultry production, litter management is prompted and should be prioritized. Poultry litter can serve as an energy feedstock for space heating and electricity generation. Currently, heating systems in use depend on electricity, charcoal or diesel which are very expensive leading to high energy costs in poultry production. The purpose of this study was therefore to investigate the potential of combustion of poultry litter for space heating in poultry production. A brazier with 32 holes, of a diameter of 1cm, on its sides comprising of dimensions; Diameter=8 cm and Height=65 cm was used to burn 1kg of Poultry Litter Briquettes. The briquettes were made with a mincer which had a nozzle of dimensions, Length=11 cm and Diameter= 25 mm producing briquettes of a diameter of 25 mm and a length of 10 cm. The briquettes were made from poultry litter of chickens at the ages of 4, 5 and 6 weeks, and were either sun or solar dried. They were then directly combusted in the brazier and heat distribution was measured at distances of 30 cm, 60 cm and 90 cm from the brazier. Charcoal was used as a control. The maximum average temperatures recorded on the surface of the brazier for week 4, 5, 6 were 471oC, 491oC, 493oC respectively; whereas for charcoal was 555oC. However, the poultry litter briquettes were not able to sustain high temperatures for long compared to charcoal. Complete combustion took an average of 120 minutes while charcoal took an average of 180 minutes.

Stock Market Prediction using Data Mining Techniques

2020 ◽  
pp. 310-313
Author(s):  
Karunesh Makker ◽  
Prince Patel ◽  
Hrishikesh Roy ◽  
Sonali Borse
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Prediction Model ◽  
Efficient Market Hypothesis ◽  
Deterministic System ◽  
Vast Number ◽  
Factors Influencing ◽  
Number Of Factors ◽  
Using Data ◽  
Robust Prediction

Stock market is a very volatile in-deterministic system with vast number of factors influencing the direction of trend on varying scales and multiple layers. Efficient Market Hypothesis (EMH) states that the market is unbeatable. This makes predicting the uptrend or downtrend a very challenging task. This research aims to combine multiple existing techniques into a much more robust prediction model which can handle various scenarios in which investment can be beneficial. Existing techniques like sentiment analysis or neural network techniques can be too narrow in their approach and can lead to erroneous outcomes for varying scenarios. By combing both techniques, this prediction model can provide more accurate and flexible recommendations. Embedding Technical indicators will guide the investor to minimize the risk and reap better returns.

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