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 …
Short-term load forecasting of microgrid via hybrid support vector regression and long short-term memory algorithms
Short-Term Load Forecasting (STLF) is the most appropriate type of forecasting for both
electricity consumers and generators. In this paper, STLF in a Microgrid (MG) is performed …
electricity consumers and generators. In this paper, STLF in a Microgrid (MG) is performed …
Short-term load forecasting for microgrid energy management system using hybrid SPM-LSTM
Load forecasting in power microgrids and load management systems is still a challenge and
needs an accurate method. Although in recent years, short-term load forecasting is done by …
needs an accurate method. Although in recent years, short-term load forecasting is done by …
A seasonal autoregressive integrated moving average with exogenous factors (SARIMAX) forecasting model-based time series approach
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 …
Short-term load forecasting for microgrid energy management system using hybrid HHO-FNN model with best-basis stationary wavelet packet transform
Accurate prediction of load has become one of the most crucial issue in the energy
management system of the microgrid. Therefore, a precise load forecasting tool is necessary …
management system of the microgrid. Therefore, a precise load forecasting tool is necessary …
Deep learning for multi-scale smart energy forecasting
T Ahmad, H Chen - Energy, 2019 - Elsevier
Short-term load prediction at the district-level is essential for feeders, substations,
consumers and transformers starts from 1-h to one-week ahead. Though, the critical problem …
consumers and transformers starts from 1-h to one-week ahead. Though, the critical problem …
Artificial neural network and SARIMA based models for power load forecasting in Turkish electricity market
ÖÖ Bozkurt, G Biricik, ZC Tayşi - PloS one, 2017 - journals.plos.org
Load information plays an important role in deregulated electricity markets, since it is the
primary factor to make critical decisions on production planning, day-to-day operations, unit …
primary factor to make critical decisions on production planning, day-to-day operations, unit …
Using gated recurrent unit network to forecast short-term load considering impact of electricity price
W Wu, W Liao, J Miao, G Du - Energy Procedia, 2019 - Elsevier
The volatility of renewable energy and the time variability of load bring challenges to load
forecasting. To improve the accuracy of prediction, the paper use gated recurrent unit (GRU) …
forecasting. To improve the accuracy of prediction, the paper use gated recurrent unit (GRU) …
Short-term electricity demand forecasting using autoregressive based time varying model incorporating representative data adjustment
This paper presents the development of an autoregressive based time varying (ARTV)
model to forecast electricity demand in a short-term period. The ARTV model is developed …
model to forecast electricity demand in a short-term period. The ARTV model is developed …
Data-driven energy storage scheduling to minimise peak demand on distribution systems with pv generation
The growing adoption of decentralised renewable energy generation (such as solar
photovoltaic panels and wind turbines) and low-carbon technologies will increase the strain …
photovoltaic panels and wind turbines) and low-carbon technologies will increase the strain …