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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 …
A deep learning framework for building energy consumption forecast
Increasing global building energy demand, with the related economic and environmental
impact, upsurges the need for the design of reliable energy demand forecast models. This …
impact, upsurges the need for the design of reliable energy demand forecast models. This …
[HTML][HTML] Multiobjective optimization of building energy consumption and thermal comfort based on integrated BIM framework with machine learning-NSGA II
Detailed parametric analysis and measurements are required to reduce building energy
usage while maintaining acceptable thermal conditions. This research suggested a system …
usage while maintaining acceptable thermal conditions. This research suggested a system …
Predicting residential energy consumption using CNN-LSTM neural networks
The rapid increase in human population and development in technology have sharply
raised power consumption in today's world. Since electricity is consumed simultaneously as …
raised power consumption in today's world. Since electricity is consumed simultaneously as …
Artificial intelligence for Sustainable Development Goals: Bibliometric patterns and concept evolution trajectories
The development of artificial intelligence (AI) as a field has impacted almost all aspects of
human life. More recently it has found a role in addressing developmental challenges …
human life. More recently it has found a role in addressing developmental challenges …
A review of data-driven building energy consumption prediction studies
K Amasyali, NM El-Gohary - Renewable and Sustainable Energy Reviews, 2018 - Elsevier
Energy is the lifeblood of modern societies. In the past decades, the world's energy
consumption and associated CO 2 emissions increased rapidly due to the increases in …
consumption and associated CO 2 emissions increased rapidly due to the increases in …
Improving the prediction of heating energy consumed at residential buildings using a combination of support vector regression and meta-heuristic algorithms
The growing population has caused to increase in energy demand worldwide. Since
significant energy consumption in the residential building sector is assigned to the heating …
significant energy consumption in the residential building sector is assigned to the heating …
Deep learning framework to forecast electricity demand
The increasing world population and availability of energy hungry smart devices are major
reasons for alarmingly high electricity consumption in the current times. So far, various …
reasons for alarmingly high electricity consumption in the current times. So far, various …
Day-ahead building-level load forecasts using deep learning vs. traditional time-series techniques
Load forecasting problems have traditionally been addressed using various statistical
methods, among which autoregressive integrated moving average with exogenous inputs …
methods, among which autoregressive integrated moving average with exogenous inputs …
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 …