Buildings account for a significant portion of the global energy consumption. Forecasting
personnel occupancy is critical for reducing energy consumption in buildings. This study …

The building sector accounts for a significant proportion of global energy usage and carbon
dioxide emissions. It is important to explore technological advances to curtail building …

This paper proposes a novel method namely WaveletKernelNet-Convolutional Block
Attention Module-BiLSTM for intelligent fault diagnosis of drilling pumps. Initially, the random …

Accurately modeling and forecasting electricity consumption remains a challenging task due
to the large number of the statistical properties that characterize this time series such as …

Due to the recent advancements in the Internet of Things and data science techniques, a
wide range of studies have investigated the use of data mining (DM) and machine learning …

This study proposes a hybrid prediction model using sparrow search algorithm (SSA) to
optimize the convolutional neural network (CNN) and support vector machine (SVM), in …

The escalating energy and environmental crises underline the imperative for sustainable
cities and societies. For effective and real-time energy management, this study proposes an …

Effective building energy prediction is vital for sustainable development, especially with an
increasing focus on flexibility and elasticity in building energy usage. However, challenges …

Sufficient building operational data serve as the key premise to enable the development of
reliable data-driven technologies for building energy management. Considering that …

Data-driven models are widely used for building-energy predictions (BEPs). In practice,
these models may fail when the available data on the target building is insufficient. Transfer …