A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids
Microgrids have recently emerged as a building block for smart grids combining distributed
renewable energy sources (RESs), energy storage devices, and load management …
renewable energy sources (RESs), energy storage devices, and load management …
Tackling climate change with machine learning
Climate change is one of the greatest challenges facing humanity, and we, as machine
learning (ML) experts, may wonder how we can help. Here we describe how ML can be a …
learning (ML) experts, may wonder how we can help. Here we describe how ML can be a …
Building energy consumption prediction for residential buildings using deep learning and other machine learning techniques
The high proportion of energy consumed in buildings has engendered the manifestation of
many environmental problems which deploy adverse impacts on the existence of mankind …
many environmental problems which deploy adverse impacts on the existence of mankind …
A global model of hourly space heating and cooling demand at multiple spatial scales
Accurate modelling of the weather's temporal and spatial impacts on building energy
demand is critical to decarbonizing energy systems. Here we introduce a customizable …
demand is critical to decarbonizing energy systems. Here we introduce a customizable …
[HTML][HTML] Review of urban building energy modeling (UBEM) approaches, methods and tools using qualitative and quantitative analysis
The world has witnessed a significant population shift to urban areas over the past few
decades. Urban areas account for about two-thirds of the world's total primary energy …
decades. Urban areas account for about two-thirds of the world's total primary energy …
A review of the-state-of-the-art in data-driven approaches for building energy prediction
Building energy prediction plays a vital role in developing a model predictive controller for
consumers and optimizing energy distribution plan for utilities. Common approaches for …
consumers and optimizing energy distribution plan for utilities. Common approaches for …
Statistical and Machine Learning forecasting methods: Concerns and ways forward
Machine Learning (ML) methods have been proposed in the academic literature as
alternatives to statistical ones for time series forecasting. Yet, scant evidence is available …
alternatives to statistical ones for time series forecasting. Yet, scant evidence is available …
Machine learning applications in urban building energy performance forecasting: A systematic review
In developed countries, buildings are involved in almost 50% of total energy use and 30% of
global green-house gas emissions. Buildings' operational energy is highly dependent on …
global green-house gas emissions. Buildings' operational energy is highly dependent on …
State of the art of machine learning models in energy systems, a systematic review
Machine learning (ML) models have been widely used in the modeling, design and
prediction in energy systems. During the past two decades, there has been a dramatic …
prediction in energy systems. During the past two decades, there has been a dramatic …
Urban heat island impacts on building energy consumption: A review of approaches and findings
Urban heat island (UHI) could have significant impacts on building energy consumption by
increasing space cooling demand and decreasing space heating demand. However, the …
increasing space cooling demand and decreasing space heating demand. However, the …