A deep learning framework for building energy consumption forecast
Increasing global building energy demand, with the related economic and environmental
impact, upsurges the need for the design of reliable energy demand forecast models. This …
impact, upsurges the need for the design of reliable energy demand forecast models. This …
A review of deep learning for renewable energy forecasting
As renewable energy becomes increasingly popular in the global electric energy grid,
improving the accuracy of renewable energy forecasting is critical to power system planning …
improving the accuracy of renewable energy forecasting is critical to power system planning …
Applications of random forest in multivariable response surface for short-term load forecasting
GF Fan, LZ Zhang, M Yu, WC Hong, SQ Dong - International Journal of …, 2022 - Elsevier
Accurate load forecasting is helpful for optimizing the use of power resources. To this end,
this investigation proposes a hybrid model for short-term load forecasting, namely the RF …
this investigation proposes a hybrid model for short-term load forecasting, namely the RF …
A novel stacked generalization ensemble-based hybrid LGBM-XGB-MLP model for Short-Term Load Forecasting
This paper proposes an effective computing framework for Short-Term Load Forecasting
(STLF). The proposed technique copes with the stochastic variations of the load demand …
(STLF). The proposed technique copes with the stochastic variations of the load demand …
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 …
Short-term rainfall forecasting using machine learning-based approaches of PSO-SVR, LSTM and CNN
Short-term rainfall forecasting plays an important role in hydrologic modeling and water
resource management problems such as flood warning and real time control of urban …
resource management problems such as flood warning and real time control of urban …
A review of the-state-of-the-art in data-driven approaches for building energy prediction
Building energy prediction plays a vital role in develo** a model predictive controller for
consumers and optimizing energy distribution plan for utilities. Common approaches for …
consumers and optimizing energy distribution plan for utilities. Common approaches for …
Bayesian optimization based dynamic ensemble for time series forecasting
Among various time series (TS) forecasting methods, ensemble forecast is extensively
acknowledged as a promising ensemble approach achieving great success in research and …
acknowledged as a promising ensemble approach achieving great success in research and …
A hybrid attention-based deep learning approach for wind power prediction
Renewable energy, especially wind power, is a practicable and promising solution to
mitigate the existing dilemma associated with climate change. Efficient and accurate …
mitigate the existing dilemma associated with climate change. Efficient and accurate …
Residential load forecasting based on LSTM fusing self-attention mechanism with pooling
Day-ahead residential load forecasting is crucial for electricity dispatch and demand
response in power systems. Electrical loads are characterized by volatility and uncertainty …
response in power systems. Electrical loads are characterized by volatility and uncertainty …