[HTML][HTML] Physical energy and data-driven models in building energy prediction: A review
The difficulty in balancing energy supply and demand is increasing due to the growth of
diversified and flexible building energy resources, particularly the rapid development of …
diversified and flexible building energy resources, particularly the rapid development of …
A review of machine learning in building load prediction
The surge of machine learning and increasing data accessibility in buildings provide great
opportunities for applying machine learning to building energy system modeling and …
opportunities for applying machine learning to building energy system modeling and …
Modeling and forecasting building energy consumption: A review of data-driven techniques
Building energy consumption modeling and forecasting is essential to address buildings
energy efficiency problems and take up current challenges of human comfort, urbanization …
energy efficiency problems and take up current challenges of human comfort, urbanization …
A review on the integrated optimization techniques and machine learning approaches for modeling, prediction, and decision making on integrated energy systems
The optimal co-planning of the integrated energy system (IES) and machine learning (ML)
application on the multivariable prediction of IES parameters have mostly been carried out …
application on the multivariable prediction of IES parameters have mostly been carried out …
A review on time series forecasting techniques for building energy consumption
Energy consumption forecasting for buildings has immense value in energy efficiency and
sustainability research. Accurate energy forecasting models have numerous implications in …
sustainability research. Accurate energy forecasting models have numerous implications in …
[HTML][HTML] A review of data mining technologies in building energy systems: Load prediction, pattern identification, fault detection and diagnosis
With the advent of the era of big data, buildings have become not only energy-intensive but
also data-intensive. Data mining technologies have been widely utilized to release the …
also data-intensive. Data mining technologies have been widely utilized to release the …
[HTML][HTML] Energy consumption prediction by using machine learning for smart building: Case study in Malaysia
Abstract Building Energy Management System (BEMS) has been a substantial topic
nowadays due to its importance in reducing energy wastage. However, the performance of …
nowadays due to its importance in reducing energy wastage. However, the performance of …
Deep-learning forecasting method for electric power load via attention-based encoder-decoder with bayesian optimization
Short-term electrical load forecasting plays an important role in the safety, stability, and
sustainability of the power production and scheduling process. An accurate prediction of …
sustainability of the power production and scheduling process. An accurate prediction of …
Validation of calibrated energy models: Common errors
G Ramos Ruiz, C Fernandez Bandera - Energies, 2017 - mdpi.com
Nowadays, there is growing interest in all the smart technologies that provide us with
information and knowledge about the human environment. In the energy field, thanks to the …
information and knowledge about the human environment. In the energy field, thanks to the …
[PDF][PDF] Methods and models for electric load forecasting: a comprehensive review
Electric load forecasting (ELF) is a vital process in the planning of the electricity industry and
plays a crucial role in electric capacity scheduling and power systems management and …
plays a crucial role in electric capacity scheduling and power systems management and …