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|Title:||Dynamic energy demand prediction and related control system for UK households|
|Abstract:||Domestic energy consumption is not only based on the type of appliances, weather conditions, and house type; it is also highly depended on related occupancy profiles. In order to manage and optimise energy generation and the effective use of energy storage, it is important to be able to accurately predict energy demand in advance. However, high-resolution (like below 1-min) occupancy profiles for domestic UK households are not ideally possible to be recorded or measured in nature. Therefore, an alternative approach to transfer particular electricity load to the number of active occupancy during selected time interval is identified by analysing the average electricity consumption of occupancy in this study. Real load data analysis for three type of participated UK households is presented throughout the year. Then the seasonal synthetic high-resolution (30s) occupancy patterns for each household are generated independently. Weekday occupancy profiles are collected seasonally and used in a Markov-Chain model to produce particular occupancy daily activity sequence for each household. A stochastic model by using Markov-Chain Monte Carlo is presented to randomly generate high-resolution occupancy profiles in dynamic. Then the predicted electricity loads are produced by mapping occupancy profiles to average electricity consumption. By validating the predicted results, it is found that maximum of sub-hourly aggregate result can mostly cover the measured demand in advance. Therefore, it is set the sub-hourly electricity demand boundary independently for each household during weekday throughout the year. Heat demand for each household is simulated in sub-hourly resolution by using DesignBuilder with EnergyPlus throughout the year. Thus, sub-hourly energy demand of each household is applied in the control system of Bio-fuel Micro Trigeneration with Hybrid Electrical Energy Storage. The control system is designed and implemented by using Siemens software STEP-7 S-300 and WinCC. In addition, the predicted energy demands are utilized into the optimization of the control system. The comparison of optimized and general control strategies shows that optimized strategies by applying prescient sub-hourly energy demand can improve system efficiency significantly.|
|Appears in Collections:||Newcastle Institute for Research on Sustainability|
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|Li, Y. 2015.pdf||Thesis||13.11 MB||Adobe PDF||View/Open|
|dspacelicence.pdf||Licence||43.82 kB||Adobe PDF||View/Open|
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