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 …
Comparing various combined techniques at seasonal autoregressive integrated moving average (SARIMA) for electrical load forecasting
The objective of this study is to investigate the accuracy of forecasting electrical consumption
using various combined techniques at the seasonal autoregressive integrated moving …
using various combined techniques at the seasonal autoregressive integrated moving …
Data analytics for forecasting cell congestion on LTE networks
This paper presents a methodology for forecasting the average downlink throughput for an
LTE cell by using real measurement data collected by multiple LTE probes. The approach …
LTE cell by using real measurement data collected by multiple LTE probes. The approach …
Short-term forecasting electricity load by long short-term memory and reinforcement learning for optimization of hyper-parameters
Electricity load forecasting is an essential operation of the power system. Deep learning is
used to improve accurate electricity load forecasting. In this study, combining Long short …
used to improve accurate electricity load forecasting. In this study, combining Long short …
[PDF][PDF] 融合ARIMA 模型和GAWNN 的溶解氧含量预测方法
吴静， 李振波， 朱玲， 李晨 - 农业机械学报, 2017 - labxing.com
针对河流污染治理, 水源管理, 提出了融合差分自回归滑动平均ARIMA 模型和遗传算法优化的
小波神经网络相结合的河流水质预测方法. 将采集的河流水质参数时间序列数据 …
小波神经网络相结合的河流水质预测方法. 将采集的河流水质参数时间序列数据 …
A time‐efficient shrinkage algorithm for the Fourier‐based prediction enabling proactive optimisation in software‐defined networks
This paper focuses on the problem of time‐efficient traffic prediction. The prediction enables
the proactive and globally scoped optimisation in software‐defined networks (SDNs). We …
the proactive and globally scoped optimisation in software‐defined networks (SDNs). We …
Household Electrical Load Forecasting: a Hybrid of Linear Models and Radial Basis Function Neural Network
This paper aims to investigate the comparison of forecasting household electrical load in J
ember, Indonesia, using a linear model and hybrid of the linear model and radial basis …
ember, Indonesia, using a linear model and hybrid of the linear model and radial basis …
A Survey on Time Series Forecasting
X He - 3D Imaging—Multidimensional Signal Processing and …, 2023 - Springer
Time series data are widely available in finance, transportation, tourism, and other vital fields
and often reflect the dynamic pattern of the observed objects. Scientific and accurate time …
and often reflect the dynamic pattern of the observed objects. Scientific and accurate time …
Application of Machine Learning Algorithms for Operational Forecasting Load Curve
A Cheremnykh, A Sidorova, A Rusina… - … Science of Riga …, 2021 - ieeexplore.ieee.org
Forecasting load curves is the most important task in the electrical energy industry. The
quality of forecasting load curves depends on reliability and efficiency of power distribution …
quality of forecasting load curves depends on reliability and efficiency of power distribution …
[PDF][PDF] ARIMA models in electrical load forecasting and their robustness to noise. Energies 2021, 14, 7952
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 …