[HTML][HTML] Interpretable machine learning for building energy management: A state-of-the-art review
Abstract Machine learning has been widely adopted for improving building energy efficiency
and flexibility in the past decade owing to the ever-increasing availability of massive building …
and flexibility in the past decade owing to the ever-increasing availability of massive building …
Recent development in electricity price forecasting based on computational intelligence techniques in deregulated power market
The development of artificial intelligence (AI) based techniques for electricity price
forecasting (EPF) provides essential information to electricity market participants and …
forecasting (EPF) provides essential information to electricity market participants and …
[HTML][HTML] Two-step deep learning framework with error compensation technique for short-term, half-hourly electricity price forecasting
Prediction of electricity price is crucial for national electricity markets supporting sale prices,
bidding strategies, electricity dispatch, control and market volatility management. High …
bidding strategies, electricity dispatch, control and market volatility management. High …
Centralized decomposition approach in LSTM for Bitcoin price prediction
E Koo, G Kim - Expert Systems with Applications, 2024 - Elsevier
It has been reported that integrating time-series decomposition methods and neural network
models improves financial time-series prediction performance. Despite its practical …
models improves financial time-series prediction performance. Despite its practical …
[HTML][HTML] Weather conditions, climate change, and the price of electricity
We estimate the effect of temperature, wind speed, solar radiation, and precipitation on
wholesale electricity prices for six European countries, analyzing the full distribution of the …
wholesale electricity prices for six European countries, analyzing the full distribution of the …
Deep and machine learning models to forecast photovoltaic power generation
The integration and management of distributed energy resources (DERs), including
residential photovoltaic (PV) production, coupled with the widespread use of enabling …
residential photovoltaic (PV) production, coupled with the widespread use of enabling …
A pattern classification methodology for interval forecasts of short-term electricity prices based on hybrid deep neural networks: A comparative analysis
Z Shao, Y Yang, Q Zheng, K Zhou, C Liu, S Yang - Applied Energy, 2022 - Elsevier
Precisely identifying multidimensional trends and hidden characteristics that relate to short-
term electricity price fluctuations and providing reliable predictions of future trends are …
term electricity price fluctuations and providing reliable predictions of future trends are …
[HTML][HTML] Intrinsically interpretable machine learning-based building energy load prediction method with high accuracy and strong interpretability
Black-box models have demonstrated remarkable accuracy in forecasting building energy
loads. However, they usually lack interpretability and do not incorporate domain knowledge …
loads. However, they usually lack interpretability and do not incorporate domain knowledge …
Forecasting of short-term load using the MFF-SAM-GCN model
Y Zou, W Feng, J Zhang, J Li - Energies, 2022 - mdpi.com
Short-term load forecasting plays a significant role in the operation of power systems.
Recently, deep learning has been generally employed in short-term load forecasting …
Recently, deep learning has been generally employed in short-term load forecasting …
Day-ahead electricity price forecasting employing a novel hybrid frame of deep learning methods: A case study in NSW, Australia
YQ Tan, YX Shen, XY Yu, X Lu - Electric Power Systems Research, 2023 - Elsevier
Day-ahead electricity price forecasting plays a vital role in electricity markets under
liberalization and deregulation, which can provide references for participants in bidding …
liberalization and deregulation, which can provide references for participants in bidding …