Notre boutique utilise des cookies pour améliorer l'expérience utilisateur et nous vous recommandons d'accepter leur utilisation pour profiter pleinement de votre navigation.
Lighting Load pattern in residential buildings varies from home to home due to non-linearity in usage patterns at specific time of use.
The uncertainty associated with lighting usage which may be linked to behavioral issue needs a better understanding and analysis (variable factors included) approach instead of assumption for improved lighting demand profile development and prediction.
Neural Network and Adaptive Neuro Fuzzy Inference System (ANFIS) are the proposed methodologies applied in this book.
The characteristics variables considered are active occupancy, natural lighting and income level.
The resultant outputs achieved using the two techniques separately showed that ANFIS prediction are much closer to the actual values as compared to neural network for most of the time of use within the 24 hour period due to its reasoning capability.
However,the combined effect of the two methodologies gives a better optimal prediction value in comparison with the actual value over the 24-hour interval period.
Tiisetso Mafolo - B.Tech graduate of Electrical Engineering, Tshwane University of Technology.Dr Olawale Popoola - Senior lecturer at Tshwane University of Technology, Pretoria, South Africa.
Research interests includes Energy Management, Energy and Behaviour, Renewable Energy, Quality Management, Power Systems and Power Electronics & Laser.
Attention : dernières pièces disponibles !
Date de disponibilité: