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 …
Machine Learning, Deep Learning and Statistical Analysis for forecasting building energy consumption—A systematic review
The building sector accounts for 36% of the total global energy usage and 40% of
associated Carbon Dioxide emissions. Therefore, the forecasting of building energy …
associated Carbon Dioxide emissions. Therefore, the forecasting of building energy …
Short-term load forecasting based on LSTM networks considering attention mechanism
J Lin, J Ma, J Zhu, Y Cui - International Journal of Electrical Power & Energy …, 2022 - Elsevier
Reliable and accurate zonal electricity load forecasting is essential for power system
operation and planning. Probabilistic load forecasts can present more comprehensive …
operation and planning. Probabilistic load forecasts can present more comprehensive …
Review of family-level short-term load forecasting and its application in household energy management system
P Ma, S Cui, M Chen, S Zhou, K Wang - Energies, 2023 - mdpi.com
With the rapid development of smart grids and distributed energy sources, the home energy
management system (HEMS) is becoming a hot topic of research as a hub for connecting …
management system (HEMS) is becoming a hot topic of research as a hub for connecting …
Short-term load forecasting with deep residual networks
We present in this paper a model for forecasting short-term electric load based on deep
residual networks. The proposed model is able to integrate domain knowledge and …
residual networks. The proposed model is able to integrate domain knowledge and …
On short-term load forecasting using machine learning techniques and a novel parallel deep LSTM-CNN approach
Since electricity plays a crucial role in countries' industrial infrastructures, power companies
are trying to monitor and control infrastructures to improve energy management and …
are trying to monitor and control infrastructures to improve energy management and …
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 …
A transformer-based method of multienergy load forecasting in integrated energy system
Multienergy load forecasting technique is the basis for the operation and scheduling of
integrated energy system. Different types of loads in an integrated energy system, ie …
integrated energy system. Different types of loads in an integrated energy system, ie …
Vector field-based support vector regression for building energy consumption prediction
Building energy consumption prediction plays an irreplaceable role in energy planning,
management, and conservation. Data-driven approaches, such as artificial neural networks …
management, and conservation. Data-driven approaches, such as artificial neural networks …
Robust short-term electrical load forecasting framework for commercial buildings using deep recurrent neural networks
This paper presents a robust short-term electrical load forecasting framework that can
capture variations in building operation, regardless of building type and location. Nine …
capture variations in building operation, regardless of building type and location. Nine …