Dec 23, 2021 · In this work, we propose to utilize machine learning algorithms to predict the hourly-based electrical power generated by a CCPP. For this, the generated power is considered a function of four fundamental parameters which are relative humidity, atmospheric pressure, ambient temperature, and exhaust vacuum.

Dec 17, 2024 · In recent years, the application of machine learning and deep learning methods for PV power prediction has attracted considerable interest within the academic community. Machine learning models can automatically process and analyze large datasets, making predictions based on historical data and various features.

1 day ago · Accurate power load forecasting helps reduce energy waste and improve grid stability. This paper proposes a hybrid forecasting model, BiStacking+TCN-GRU, which leverages both ensemble learning and deep learning techniques. The model first applies the Pearson correlation coefficient (PCC) to select features highly correlated with the power load. Then, BiStacking is used for preliminary ...

Apr 29, 2020 · Although using various types of sensors easily captures weather data, the quantity and quality issues from those information create a PV forecasting challenge. For instance, solar power generation as renewable energy is highly dependent in the irradiance of the sun.

Aug 19, 2024 · The growing integration of renewable energy sources into grid-connected microgrids has created new challenges in power generation forecasting and energy management. This paper explores the use...

3 days ago · Existing LSTM-based power quality (PQ) prediction models primarily rely on historical information, which limits their ability to fully capture contextual dependencies. ... Experiments were conducted using power quality data from Nanchang as the primary dataset, with additional datasets from Nanjing, Wuhan, Changsha, and Beijing used for ...

Apr 21, 2025 · Ramu, P. & Gangatharan, S. An ensemble machine learning-based solar power prediction of meteorological variability conditions to improve accuracy in forecasting. J. Chin. Inst. Eng. 46, 737–753 ...

Feb 22, 2025 · Power demand estimation in Tetouan, Morocco, uses fuzzy clustering with machine learning-based time series forecasting models as the main subject of research. This paper tackles an important...

5 days ago · Machine learning offers a versatile toolkit for enabling predictive power management across various applications and system scales. By learning from historical patterns and real-time data, ML models can anticipate future energy needs, detect anomalies, and continuously adapt to evolving workloads.

2 days ago · Machine learning, a subset of artificial intelligence, encompasses algorithms and techniques that enable computers to learn from data and make predictions or decisions without explicit programming. ML algorithms can identify patterns and relationships within datasets, allowing them to generate predictive models that capture the underlying ...