A review of ARIMA vs. machine learning approaches for time series forecasting in data driven networks
VI Kontopoulou, AD Panagopoulos, I Kakkos… - Future Internet, 2023 - mdpi.com
In the broad scientific field of time series forecasting, the ARIMA models and their variants
have been widely applied for half a century now due to their mathematical simplicity and …
have been widely applied for half a century now due to their mathematical simplicity and …
Forecasting of future greenhouse gas emission trajectory for India using energy and economic indexes with various metaheuristic algorithms
The accelerating increment of greenhouse gas (GHG) concentration in the atmosphere
already reached an alarming level, and nowadays its adverse impacts on the living …
already reached an alarming level, and nowadays its adverse impacts on the living …
Short-term electricity load forecasting with machine learning
E Aguilar Madrid, N Antonio - Information, 2021 - mdpi.com
An accurate short-term load forecasting (STLF) is one of the most critical inputs for power
plant units' planning commitment. STLF reduces the overall planning uncertainty added by …
plant units' planning commitment. STLF reduces the overall planning uncertainty added by …
Arima models in electrical load forecasting and their robustness to noise
The paper addresses the problem of insufficient knowledge on the impact of noise on the
auto-regressive integrated moving average (ARIMA) model identification. The work offers a …
auto-regressive integrated moving average (ARIMA) model identification. The work offers a …
Short-term load forecasting based on the transformer model
From the perspective of energy providers, accurate short-term load forecasting plays a
significant role in the energy generation plan, efficient energy distribution process and …
significant role in the energy generation plan, efficient energy distribution process and …
Performance evaluation of the impact of clustering methods and parameters on adaptive neuro-fuzzy inference system models for electricity consumption prediction …
Increasing economic and population growth has led to a rise in electricity consumption.
Consequently, electrical utility firms must have a proper energy management strategy in …
Consequently, electrical utility firms must have a proper energy management strategy in …
Forecasting COVID-19 recovered cases with Artificial Neural Networks to enable designing an effective blood supply chain
This study introduces a forecasting model to help design an effective blood supply chain
mechanism for tackling the COVID-19 pandemic. In doing so, first, the number of people …
mechanism for tackling the COVID-19 pandemic. In doing so, first, the number of people …
Robust wavelet transform neural-network-based short-term load forecasting for power distribution networks
Y Wang, P Guo, N Ma, G Liu - Sustainability, 2022 - mdpi.com
A precise short-term load-forecasting model is vital for energy companies to create accurate
supply plans to reduce carbon dioxide production, causing our lives to be more …
supply plans to reduce carbon dioxide production, causing our lives to be more …
Short-Term Residential Load Forecasting with Baseline-Refinement Profiles and Bi-Attention Mechanism
JW Xiao, P Liu, H Fang, XK Liu… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
With the development of smart grid and renewable energy technologies, residential load
forecasting has become an increasingly important task. Short-term residential load …
forecasting has become an increasingly important task. Short-term residential load …
[HTML][HTML] Prediction of electricity consumption during epidemic period based on improved particle swarm optimization algorithm
X Li, Y Wang, G Ma, X Chen, J Fan, B Yang - Energy Reports, 2022 - Elsevier
A prediction method of electricity consumption is developed in order to address the
problems of big change and imbalance in electricity consumption caused by COVID-19. In …
problems of big change and imbalance in electricity consumption caused by COVID-19. In …