The residential sector represents some 30% of global electricity consumption but the underlying composition and drivers are still only poorly understood. The drivers are many, varied, and complex, including local climate, household demographics, household behaviour, building stock and the type and number of appliances.
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.
Desiccant wheel based air-conditioning systems (DWAC) include a desiccant wheel component that performs latent cooling coupled to another component, for example an indirect evaporative cooler (IEC also known as a dew point evaporative cooler), that performs the sensible cooling without adding moisture into the air flow.
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).
The increasing penetration of solar power into the electricity grid has created the need for accurate solar power forecasts for facilitating safe and reliable electricity grid management. Numerical Weather Prediction (NWP) is a common method for forecasting solar irradiance beyond several hours ahead, which is crucial, particularly for applications in...
This presentation investigates the value of using forecast variables from multiple vertical layers of NWP as machine learning inputs in improving the accuracy of solar irradiance forecasts. Moreover, the effects of postprocessing on the NWP models with different spatio-temporal resolution – Global Forecast System (GFS), regional (ACCESS-R) and city (ACCESS-C)...