Electricity Use For Lighting Research Articles

A Methodology for Defining Electricity Demand in Energy Simulations Referred to the Italian Context

Electricity consumption in Europe is constantly increasing, despite the fact that in recent years, huge efforts in terms of programs and regulations have been made towards energy demand reduction and energy systems improvement. Since the electricity demand affects both the operation of the supply and distribution plants and the thermal loads of buildings, the importance of providing a proper definition of demand profiles is evident. The main aim of the paper is to provide a set of standard electricity profiles that can reasonably be adopted as input in energy simulations related to the built environment, with particular regards to the Italian context. The work presented in this paper originated within a wider long lasting research aimed at developing a platform for buildings’ energy simulations at district level, with particular reference to the Italian conditions. In this context, it was necessary to define hourly profiles regarding both occupancy and electricity use for lighting and appliances related to different building uses and typologies. For this purpose, the main methods and references for defining electricity loads in buildings were evaluated and average hourly profiles were accordingly developed for residential and commercial buildings. Then the related internal gains were determined and compared to the current Italian standards.

Artificial neural networks for energy analysis of office buildings with daylighting

An artificial neural network (ANN) model was developed for office buildings with daylighting for subtropical climates. A total of nine variables were used as the input parameters – four variables were related to the external weather conditions (daily average dry-bulb temperature, daily average wet-bulb temperature, daily global solar radiation and daily average clearness index), four for the building envelope designs (solar aperture, daylight aperture, overhang and side-fins projections), and the last variable was day type (i.e. weekdays, Saturdays and Sundays). There were four nodes at the output layer with the estimated daily electricity use for cooling, heating, electric lighting and total building as the output. Building energy simulation using EnergyPlus was conducted to generate daily building energy use database for the training and testing of ANNs. The Nash–Sutcliffe efficiency coefficient for the ANN modelled cooling, heating, electric lighting and total building electricity use was 0.994, 0.940, 0.993, and 0.996, respectively, indicating excellent predictive power. Error analysis showed that lighting electricity use had the smallest errors, from 0.2% under-estimation to 3.6% over-estimation, with the coefficient of variation of the root mean square error ranging from 3% to 5.6%.