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[HTML][HTML] Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark
While the field of electricity price forecasting has benefited from plenty of contributions in the
last two decades, it arguably lacks a rigorous approach to evaluating new predictive …
last two decades, it arguably lacks a rigorous approach to evaluating new predictive …
Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx
We extend neural basis expansion analysis (NBEATS) to incorporate exogenous factors.
The resulting method, called NBEATSx, improves on a well-performing deep learning …
The resulting method, called NBEATSx, improves on a well-performing deep learning …
Electricity price forecasting on the day-ahead market using machine learning
The price of electricity on the European market is very volatile. This is due both to its mode of
production by different sources, each with its own constraints (volume of production …
production by different sources, each with its own constraints (volume of production …
Electricity price prediction based on hybrid model of adam optimized LSTM neural network and wavelet transform
Z Chang, Y Zhang, W Chen - Energy, 2019 - Elsevier
To a large extent, electricity price prediction is a daunting task because it depends on
factors, such as weather, fuel, load and bidding strategies etc. Those features generate a lot …
factors, such as weather, fuel, load and bidding strategies etc. Those features generate a lot …
Machine learning in energy economics and finance: A review
Abstract Machine learning (ML) is generating new opportunities for innovative research in
energy economics and finance. We critically review the burgeoning literature dedicated to …
energy economics and finance. We critically review the burgeoning literature dedicated to …
[HTML][HTML] Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms
In this paper, a novel modeling framework for forecasting electricity prices is proposed.
While many predictive models have been already proposed to perform this task, the area of …
While many predictive models have been already proposed to perform this task, the area of …
Daily electricity price forecasting using artificial intelligence models in the Iranian electricity market
The structure of the electricity market in Iran is based on a pay-as-bid auction mechanism. In
such a market, hydropower generators need to have accurate estimates of energy price in …
such a market, hydropower generators need to have accurate estimates of energy price in …
[HTML][HTML] 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 …
A survey on hyperparameters optimization algorithms of forecasting models in smart grid
Forecasting in the smart grid (SG) plays a vital role in maintaining the balance between
demand and supply of electricity, efficient energy management, better planning of energy …
demand and supply of electricity, efficient energy management, better planning of energy …
[HTML][HTML] Data augmentation for time series regression: Applying transformations, autoencoders and adversarial networks to electricity price forecasting
A model's expected generalisation error is inversely proportional to its training set size. This
relationship can pose a problem when modelling multivariate time series, because structural …
relationship can pose a problem when modelling multivariate time series, because structural …