Forecasting photovoltaic power generation with a stacking ensemble model
Nowadays, photovoltaics (PV) has gained popularity among other renewable energy
sources because of its excellent features. However, the instability of the system's output has …
sources because of its excellent features. However, the instability of the system's output has …
A hybrid machine learning method with explicit time encoding for improved Malaysian photovoltaic power prediction
Nowadays, with the growing interest in green energy, further improvements in photovoltaic
(PV) power systems are needed. In this regard, the main aim is to find an optimal method to …
(PV) power systems are needed. In this regard, the main aim is to find an optimal method to …
[HTML][HTML] A new framework for electricity price forecasting via multi-head self-attention and CNN-based techniques in the competitive electricity market
Due to recent technical improvements, the smart grid has become a feasible platform for
electricity market participants to successfully regulate their bidding process based on …
electricity market participants to successfully regulate their bidding process based on …
Operation optimization method of distribution network with wind turbine and photovoltaic considering clustering and energy storage
F Zheng, X Meng, L Wang, N Zhang - Sustainability, 2023 - mdpi.com
The problem of distribution network operation optimization is diversified and uncertain. In
order to solve this problem, this paper proposes a method of distribution network operation …
order to solve this problem, this paper proposes a method of distribution network operation …
Improved Active and Reactive Energy Forecasting Using a Stacking Ensemble Approach: Steel Industry Case Study
The prevalence of substantial inductive/capacitive loads within the industrial sectors induces
variations in reactive energy levels. The imbalance between active and reactive energy …
variations in reactive energy levels. The imbalance between active and reactive energy …
Electricity price forecasting one day ahead by employing hybrid deep learning model
This study proposes hybrid Deep Learning (DL) models for electricity price forecasting (EPF)
one day ahead of the Nord Pool spot electricity market. The proposed hybrid DL model …
one day ahead of the Nord Pool spot electricity market. The proposed hybrid DL model …
[HTML][HTML] Day-Ahead electricity price forecasting using a CNN-BiLSTM model in conjunction with autoregressive modeling and hyperparameter optimization
The inherent volatility in electricity prices exerts a significant impact on the dynamic nature of
the electricity market, shaping the decision-making processes of its stakeholders. Precise …
the electricity market, shaping the decision-making processes of its stakeholders. Precise …
Short-term electricity price forecasting using interpretable hybrid machine learning models
In this paper, a combination of single and hybrid Machine learning (ML) models were
proposed to forecast the electricity price one day ahead for the Nord Pool spot electricity …
proposed to forecast the electricity price one day ahead for the Nord Pool spot electricity …
Forecast of solar photovoltaic power output based on polycrystalline panel-based employing various ensemble machine learning methods
This paper presents solar photovoltaic (PV) energy prediction based on Polycrystalline
technology utilizing various ensemble machine learning (ML) models. Several ML models …
technology utilizing various ensemble machine learning (ML) models. Several ML models …
Development of methods for minimizing energy losses in electrical networks
AG Chernykh, YN Barykina… - IOP Conference Series …, 2022 - iopscience.iop.org
The article analyzes losses in terms of the volume and supply of energy in electric networks
using the final report of Rosseti Siberia for 2020. The causes of a decrease in the volume of …
using the final report of Rosseti Siberia for 2020. The causes of a decrease in the volume of …