Short-term electricity demand forecasting using deep neural networks: An analysis for Thai data
Electricity demand forecasting plays a significant role in energy markets. Accurate prediction
of electricity demand is the key factor in optimizing power generation and consumption …
of electricity demand is the key factor in optimizing power generation and consumption …
Short-term electricity demand forecasting: Impact analysis of temperature for Thailand
Accurate electricity demand forecasting for a short horizon is very important for day-to-day
control, scheduling, operation, planning, and stability of the power system. The main factors …
control, scheduling, operation, planning, and stability of the power system. The main factors …
A machine learning model ensemble for mixed power load forecasting across multiple time horizons
The increasing penetration of renewable energy sources tends to redirect the power
systems community's interest from the traditional power grid model towards the smart grid …
systems community's interest from the traditional power grid model towards the smart grid …
[PDF][PDF] Data-Driven Load Forecasting Using Machine Learning and Meteorological Data.
A Alrashidi, AM Qamar - Computer Systems Science & …, 2023 - cdn.techscience.cn
Electrical load forecasting is very crucial for electrical power systems' planning and
operation. Both electrical buildings' load demand and meteorological datasets may contain …
operation. Both electrical buildings' load demand and meteorological datasets may contain …
Forecasting Short-Term Electricity Load Using Validated Ensemble Learning
As short-term load forecasting is essential for the day-to-day operation planning of power
systems, we built an ensemble learning model to perform such forecasting for Thai data. The …
systems, we built an ensemble learning model to perform such forecasting for Thai data. The …
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 …
Fostering Energy Resilience in the Rural Thai Power System—A Case Study in Nakhon Phanom
MCG Hart, MH Breitner - Energies, 2022 - mdpi.com
With rising electricity demand, heavy reliance on imports, and recent economic downturns
due to the negative impact of the COVID-19 pandemic, supply chain bottlenecks, and the …
due to the negative impact of the COVID-19 pandemic, supply chain bottlenecks, and the …
Multi-step short-term electric load forecasting using 2D convolutional neural networks
Electric load forecasting is done at various forecasting horizons. The horizon for a short-term
load forecast (STLF) typically ranges from a few minutes up to a week. In India, the power …
load forecast (STLF) typically ranges from a few minutes up to a week. In India, the power …
A Deep Learning Approach for Short-Term Electricity Demand Forecasting: Analysis of Thailand Data
RK Shiwakoti, C Charoenlarpnopparut, K Chapagain - Applied Sciences, 2024 - mdpi.com
Accurate electricity demand forecasting serves as a vital planning tool, enhancing the
reliability of management decisions. Apart from that, achieving these aims, particularly in …
reliability of management decisions. Apart from that, achieving these aims, particularly in …
Mean shift densification of scarce data sets in short-term electric power load forecasting for special days
Short-term load forecasting plays an important role to the operation of electric systems, as a
key parameter for planning maintenances and to support the decision making process on …
key parameter for planning maintenances and to support the decision making process on …