Building energy prediction using artificial neural networks: A literature survey
C Lu, S Li, Z Lu - Energy and Buildings, 2022 - Elsevier
Building Energy prediction has emerged as an active research area due to its potential in
improving energy efficiency in building energy management systems. Essentially, building …
improving energy efficiency in building energy management systems. Essentially, building …
Taxonomy research of artificial intelligence for deterministic solar power forecasting
With the world-wide deployment of solar energy for a sustainable and renewable future, the
stochastic and volatile nature of solar power pose significant challenges to the reliable …
stochastic and volatile nature of solar power pose significant challenges to the reliable …
Wind speed forecasting based on variational mode decomposition and improved echo state network
Accurate wind speed forecasting is conducive to power system operation, peak regulation,
security analysis, and energy trading. This study proposes a hybrid model named VMD-DE …
security analysis, and energy trading. This study proposes a hybrid model named VMD-DE …
Gross electricity consumption forecasting using LSTM and SARIMA approaches: A case study of Türkiye
Gross electricity consumption (GEC) forecasts are an essential tool for policymakers in
developing countries. It is widely acknowledged that GEC forecasting models contribute …
developing countries. It is widely acknowledged that GEC forecasting models contribute …
Effective long short-term memory with differential evolution algorithm for electricity price prediction
Electric power, as an efficient and clean energy, has considerable importance in industries
and human lives. Electricity price is becoming increasingly crucial for balancing electricity …
and human lives. Electricity price is becoming increasingly crucial for balancing electricity …
[HTML][HTML] A novel approach based on integration of convolutional neural networks and echo state network for daily electricity demand prediction
Predicting electricity demand data is considered an essential task in decisions taking, and
establishing new infrastructure in the power generation network. To deliver a high-quality …
establishing new infrastructure in the power generation network. To deliver a high-quality …
Evolving deep echo state networks for intelligent fault diagnosis
Echo state network (ESN) is a fast recurrent neural network with remarkable generalization
performance for intelligent diagnosis of machinery faults. When dealing with high …
performance for intelligent diagnosis of machinery faults. When dealing with high …
Intelligent techniques for forecasting electricity consumption of buildings
The increasing trend in building sector's energy demand calls for reliable and robust energy
consumption forecasting models. This study aims to compare prediction capabilities of five …
consumption forecasting models. This study aims to compare prediction capabilities of five …
Forecasting energy consumption and wind power generation using deep echo state network
Accurate energy forecasting is of great significance for the energy sector to formulate short-
term plans and long-term development strategies for meeting energy needs. This study …
term plans and long-term development strategies for meeting energy needs. This study …
Rolling decomposition method in fusion with echo state network for wind speed forecasting
Accurate wind speed forecasting is beneficial to ensure the safe and stable operation of
power systems, improve economic benefits, and promote the healthy development of the …
power systems, improve economic benefits, and promote the healthy development of the …