[HTML][HTML] Physical energy and data-driven models in building energy prediction: A review
The difficulty in balancing energy supply and demand is increasing due to the growth of
diversified and flexible building energy resources, particularly the rapid development of …
diversified and flexible building energy resources, particularly the rapid development of …
Forecasting methods in energy planning models
KB Debnath, M Mourshed - Renewable and Sustainable Energy Reviews, 2018 - Elsevier
Energy planning models (EPMs) play an indispensable role in policy formulation and energy
sector development. The forecasting of energy demand and supply is at the heart of an EPM …
sector development. The forecasting of energy demand and supply is at the heart of an EPM …
Short-term load forecasting using EMD-LSTM neural networks with a Xgboost algorithm for feature importance evaluation
H Zheng, J Yuan, L Chen - Energies, 2017 - mdpi.com
Accurate load forecasting is an important issue for the reliable and efficient operation of a
power system. This study presents a hybrid algorithm that combines similar days (SD) …
power system. This study presents a hybrid algorithm that combines similar days (SD) …
[HTML][HTML] Electricity price forecasting: A review of the state-of-the-art with a look into the future
R Weron - International journal of forecasting, 2014 - Elsevier
A variety of methods and ideas have been tried for electricity price forecasting (EPF) over the
last 15 years, with varying degrees of success. This review article aims to explain the …
last 15 years, with varying degrees of success. This review article aims to explain the …
Electricity price forecasting using recurrent neural networks
Accurate electricity price forecasting has become a substantial requirement since the
liberalization of the electricity markets. Due to the challenging nature of electricity prices …
liberalization of the electricity markets. Due to the challenging nature of electricity prices …
Energy models for demand forecasting—A review
Energy is vital for sustainable development of any nation–be it social, economic or
environment. In the past decade energy consumption has increased exponentially globally …
environment. In the past decade energy consumption has increased exponentially globally …
[HTML][HTML] Transfer learning in demand response: A review of algorithms for data-efficient modelling and control
A number of decarbonization scenarios for the energy sector are built on simultaneous
electrification of energy demand, and decarbonization of electricity generation through …
electrification of energy demand, and decarbonization of electricity generation through …
Day-ahead electricity price forecasting via the application of artificial neural network based models
Traditionally, short-term electricity price forecasting has been essential for utilities and
generation companies. However, the deregulation of electricity markets created a …
generation companies. However, the deregulation of electricity markets created a …
Day-ahead load forecast using random forest and expert input selection
The electrical load forecast is getting more and more important in recent years due to the
electricity market deregulation and integration of renewable resources. To overcome the …
electricity market deregulation and integration of renewable resources. To overcome the …
Extended forecast methods for day-ahead electricity spot prices applying artificial neural networks
Day-ahead electricity prices are generally used as reference prices for decisions done in
energy trading, eg purchase and sale strategies are typically based on the day-ahead spot …
energy trading, eg purchase and sale strategies are typically based on the day-ahead spot …