[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 …
A literature review on dynamic pricing of electricity
Revenue management and dynamic pricing are concepts that have immense possibilities
for application in the energy sector. Both can be considered as demand-side management …
for application in the energy sector. Both can be considered as demand-side management …
Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks
We conduct an extensive empirical study on short-term electricity price forecasting (EPF) to
address the long-standing question if the optimal model structure for EPF is univariate or …
address the long-standing question if the optimal model structure for EPF is univariate or …
Short-term electricity price forecasting with stacked denoising autoencoders
A short-term forecasting of the electricity price with data-driven algorithms is studied in this
research. A stacked denoising autoencoder (SDA) model, a class of deep neural networks …
research. A stacked denoising autoencoder (SDA) model, a class of deep neural networks …
Event-driven forecasting of wholesale electricity price and frequency regulation price using machine learning algorithms
The wholesale electricity market is composed of real-time market and procurement. Since
the fully liberalization of the energy market in Singapore in 2018, competition among the …
the fully liberalization of the energy market in Singapore in 2018, competition among the …
Short-term load forecast of microgrids by a new bilevel prediction strategy
Microgrids are a rapidly growing sector of smart grids, which will be an essential component
in the trend toward distributed electricity generation. In the operation of a microgrid …
in the trend toward distributed electricity generation. In the operation of a microgrid …
Bayesian deep learning based method for probabilistic forecast of day-ahead electricity prices
The availability of accurate day-ahead energy prices forecasts is crucial to achieve a
successful participation to liberalized electricity markets. Moreover, forecasting systems …
successful participation to liberalized electricity markets. Moreover, forecasting systems …
An accurate and fast converging short-term load forecasting model for industrial applications in a smart grid
Short-term load forecasting (STLF) models are very important for electric industry in the trade
of energy. These models have many applications in the day-to-day operations of electric …
of energy. These models have many applications in the day-to-day operations of electric …
Hybrid wavelet-PSO-ANFIS approach for short-term electricity prices forecasting
A novel hybrid approach, combining wavelet transform, particle swarm optimization, and
adaptive-network-based fuzzy inference system, is proposed in this paper for short-term …
adaptive-network-based fuzzy inference system, is proposed in this paper for short-term …
Price forecasting of day-ahead electricity markets using a hybrid forecast method
Energy price forecasting in a competitive electricity market is crucial for the market
participants in planning their operations and managing their risk, and it is also the key …
participants in planning their operations and managing their risk, and it is also the key …