The surge of machine learning and increasing data accessibility in buildings provide great
opportunities for applying machine learning to building energy system modeling and …

Electricity load forecasting is an important tool which can be utilized to enable effective
control of commercial building electricity loads. Accurate forecasts of commercial building …

With the rapid growth in the volume of relevant and available data, feature engineering is
emerging as a popular research subject in data-driven building energy prediction owing to …

Accurate forecasting of energy consumption in a building is an important strategy in
achieving the goal of reducing energy demand, as well as to improve energy efficiency. To …

Artificial neural networks (ANNs) have been used for the prediction of the energy
consumption of a passive solar building. The building structure consists of one room with an …

Abstract 3D object recognition and pattern recognition are active and fast-growing research
areas in the field of computer vision. It is mandatory to define the pattern class, feature …

This paper introduces an easily implementable and computationally efficient, ARX
(autoregressive with exogenous, ie, external, inputs) time and temperature indexed model …

Abstract Information about the patterns that govern the energy demand and onsite
generation can generate significant savings in the range of 15–30% in most cases and thus …

As the energy system transitions to an intelligent smart grid with a mostly renewable energy
supply, synthetic energy time series are required to facilitate the development and …

Building thermal transfer models are essential to predict transient cooling or heating
requirements for performance monitoring, diagnosis and control strategy analysis. Detailed …