The impact of low-speed filtration on the performance of salt water chlorinators, pool cleaners, and the pool water quality, based on experimental and modelled data, is investigated. Results show that a typical salt water chlorinator and pressure pool cleaner do not work well for flow rates of less than 1 litre s -1 and 1.3 litre s -1 respectively.
The photovoltaic thermal (PV/T) driven desiccant air cooling process could be a good solution to the conventional air cooling cycle in terms of energy saving, where the latent cooling load would be removed adiabatically. However, problems exist where the required desiccant regeneration temperature (60oC - 80oC) often exceeds the outlet fluid temperature from standard PV/T collectors.
Smart grid components such as smart home and battery energy management systems, high penetration of renewable energy systems, and demand response activities, require accurate electricity demand forecasts for the successful operation of the electricity distribution networks.
Smart meter data can be used for various purposes within smart grids, including residential energy applications, such as Home Energy Management Systems (HEMS) and Battery Energy Management Systems (BEMS).
A whole system approach was adopted to optimize a residential pool filtering system. This project presents for the first time, the experimental measurements of the pool water quality (i.e., the chemical concentrations) and all energy-consuming components when operating the filtering system at low flow conditions.
Power Purchase Agreements (PPAs) with utility-scale renewable energy plants allow medium to large-scale electricity consumers to meet a proportion of their load demand using renewable electricity. This allows them to reduce their greenhouse gas (GHG) emissions, while at the same time reducing their exposure to volatile and peak prices in the National Electricity Market (NEM).
Methods and techniques for using smart meter data are analysed; forecasting clustering, classification and optimization.
End use applications of smart meter data are reviewed.
Performance of state of the art models are compared.
Challenges associated with methods and application are identified.
A new analysis guideline is proposed.