A review on renewable energy and electricity requirement forecasting models for smart grid and buildings
The benefits of renewable energy are that it is sustainable and is low in environmental
pollution. Growing load requirement, global warming, and energy crisis need energy …
pollution. Growing load requirement, global warming, and energy crisis need energy …
A new generation of AI: A review and perspective on machine learning technologies applied to smart energy and electric power systems
L Cheng, T Yu - International Journal of Energy Research, 2019 - Wiley Online Library
The new generation of artificial intelligence (AI), called AI 2.0, has recently become a
research focus. Data‐driven AI 2.0 will accelerate the development of smart energy and …
research focus. Data‐driven AI 2.0 will accelerate the development of smart energy and …
[HTML][HTML] Short term load forecasting based on ARIMA and ANN approaches
C Tarmanini, N Sarma, C Gezegin, O Ozgonenel - Energy Reports, 2023 - Elsevier
Forecasting electricity demand requires accurate and sustainable data acquisition systems
which rely on smart grid systems. To predict the demand expected by the grid, many smart …
which rely on smart grid systems. To predict the demand expected by the grid, many smart …
Do we really need deep learning models for time series forecasting?
Time series forecasting is a crucial task in machine learning, as it has a wide range of
applications including but not limited to forecasting electricity consumption, traffic, and air …
applications including but not limited to forecasting electricity consumption, traffic, and air …
Stacking ensemble learning for short-term electricity consumption forecasting
The ability to predict short-term electric energy demand would provide several benefits, both
at the economic and environmental level. For example, it would allow for an efficient use of …
at the economic and environmental level. For example, it would allow for an efficient use of …
A seasonal autoregressive integrated moving average with exogenous factors (SARIMAX) forecasting model-based time series approach
FR Alharbi, D Csala - Inventions, 2022 - mdpi.com
Time series modeling is an effective approach for studying and analyzing the future
performance of the power sector based on historical data. This study proposes a forecasting …
performance of the power sector based on historical data. This study proposes a forecasting …
Enhanced ensemble structures using wavelet neural networks applied to short-term load forecasting
Load forecasting implies directly in financial return and information for electrical systems
planning. A framework to build wavenet ensemble for short-term load forecasting is …
planning. A framework to build wavenet ensemble for short-term load forecasting is …
Online ensemble learning for load forecasting
L Von Krannichfeldt, Y Wang… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Traditionally, load forecasting models are trained offline and generate predictions online.
However, the pure batch learning approach fails to incorporate new load information …
However, the pure batch learning approach fails to incorporate new load information …
Machine learning algorithms for short-term load forecast in residential buildings using smart meters, sensors and big data solutions
In this paper, we propose a scalable Big Data framework that collects the data from smart
meters and weather sensors, pre-processes and loads it into a NoSQL database that is …
meters and weather sensors, pre-processes and loads it into a NoSQL database that is …
Research on short-term load forecasting using XGBoost based on similar days
X Liao, N Cao, M Li, X Kang - … , big data & smart city (ICITBS), 2019 - ieeexplore.ieee.org
In this paper, the power load data is increasing exponentially and the traditional forecasting
model is fatigued and difficult to achieve high efficiency when dealing with massive data. A …
model is fatigued and difficult to achieve high efficiency when dealing with massive data. A …