A winning method for day-ahead electricity demand forecasting during and after the COVID-19 pandemic.
problem Day-ahead electricity demand forecasting during and after the COVID-19 pandemic.
method Online forecast combination of multiple point prediction models with a holiday adjustment procedure and smoothed Bernstein Online Aggregation (BOA).
result Excellent forecasting performance, particularly due to the holiday adjustment procedure and fully adaptive smoothed BOA approach.
The paper simplifies electricity market curves with less parameters.
problem Modeling electricity prices and demands efficiently.
method Mesh-free interpolation using radial basis functions.
result The method reduces parameters needed to represent curves.
Study improves forecasting of aggregated curves in electricity markets.
problem Improving accuracy in predicting aggregated curves like demand and supply in electricity markets.
method Exploits hierarchical structure of aggregated curves, uses reconciliation methods (bottom-up, top-down, linear optimal, aggregated-down).
result Hierarchical reconciliation methods can significantly improve forecast accuracy of aggregated curves.
Paper models and forecasts intra-day electricity price spreads.
problem Forecasting intra-day price spreads for electricity traders and operators.
method Dynamic density functions based on skewed-t distributions, conditional on exogenous drivers.
result Best fitting and forecasting specifications selected using Pinball Loss function.
Frugal method predicts multiple local electricity loads efficiently.
problem Day-ahead forecasting of over 1000 substations in France.
method Adaptive generalized additive models with state-space representations, combined with transfer learning.
result Reduction of computational needs and emissions with competitive accuracy.
This paper formulates dynamic density functions, based upon skewed-t and similar representations, to model and forecast electricity price spreads between different hours of the day. This supports an optimal day ahead storage and discharge schedule, and thereby facilitates a bidding strategy for a merchant arbitrage fac…
This study improves electricity price forecasting in the Irish balancing market.
problem Limited and inconsistent research on short-term price forecasting in volatile balancing markets.
method Compared statistical, machine learning, and deep learning models using a public dataset and framework.
result LEAR, a statistical approach, outperforms complex models in the balancing market.
In our paper we analyze the relationship between the day-ahead electricity price of the Energy Exchange Austria (EXAA) and other day-ahead electricity prices in Europe. We focus on markets, which settle their prices after the EXAA, which enables traders to include the EXAA price into their calculations. For each market…
The liberalization of electricity markets and the development of renewable energy sources has led to new challenges for decision makers. These challenges are accompanied by an increasing uncertainty about future electricity price movements. The increasing amount of papers, which aim to model and predict electricity pri…
THieF improves day-ahead electricity price prediction accuracy by reconciling hourly and block forecasts.
problem Improving accuracy in predicting day-ahead electricity prices.
method Temporal hierarchy forecasting (THieF) reconciling hourly and block forecasts.
result THieF significantly improves accuracy (up to 13%) at all levels of prediction.
Novel approach predicts long-term seasonal component of electricity prices for improved forecasting.
problem Improving day-ahead electricity price forecasting accuracy.
method Extracts trend-seasonal pattern from extrapolated price series using autoregressive and LASSO models.
result Improves predictive accuracy by 3-15% in root mean squared error and 1% in profits.
Study finds CRPS learning doesn't improve day-ahead bidding profits despite better accuracy.
problem Improving day-ahead bidding profits through better probabilistic price forecasting.
method CRPS learning to minimize continuous ranked probability score (CRPS) for ensemble predictions.
result Higher diversity in ensemble predictions improves accuracy but doesn't lead to higher profits.
State-space models win a forecasting competition for unstable data.
problem Forecasting electricity demand during the post-covid period.
method Adapting state-space models to balance time-series adaptability and machine learning complexity.
result State-space models provide a better compromise between adaptability and accuracy for non-stationary data.
We propose a multivariate elastic net regression forecast model for German quarter-hourly electricity spot markets. While the literature is diverse on day-ahead prediction approaches, both the intraday continuous and intraday call-auction prices have not been studied intensively with a clear focus on predictive power. …
Paper proposes a new method for probabilistic electricity price forecasting.
problem Accurate estimation of forecast uncertainties for optimal decision making.
method Implicit generative ensemble post-processing using an ensemble of point forecasting models.
result Method outperforms well-established model combination benchmarks.
Neural networks outperform single-hour models in day-ahead electricity price forecasting.
problem Improving accuracy in day-ahead electricity price forecasting.
method Compared two neural network structures: one-hour models and daily auction models.
result Daily auction models outperform one-hour models in forecasting accuracy.
This review analyzes deep learning methods for electricity price forecasting across different markets.
problem Insufficient analysis of deep learning methods in electricity price forecasting.
method Unified taxonomy of deep learning components, analysis of trends across markets.
result Shift toward probabilistic, microstructure-centric, and market-aware designs.
Transfer learning improves electricity price forecasting accuracy.
problem Accurate day-ahead electricity price prediction using available data.
method Pre-train a neural network on source markets and fine-tune for target market.
result Transfer learning significantly improves forecasting performance.
Transformer model forecasts electricity price spread for virtual bidding.
problem Volatility in renewable energy causes price forecasting challenges.
method Transformer-based deep learning model using various time-series features.
result Trading strategy at peak hour yields nearly consistent profit.
Machine learning improves electricity price forecasting.
problem Predicting electricity prices in various horizons.
method Application of machine learning techniques to EPF models.
result Machine learning models outperform traditional methods.
The study analyzes how probabilistic forecasts improve battery trading strategies in electricity markets.
problem Improvements in statistical forecast quality do not directly translate to economic value in battery trading strategies.
method The study frames battery optimization as a stochastic program based on fully probabilistic forecasts and examines decision quality under different uncertainty models.
result The study identifies two critical flaws in quantile-based trading strategies and provides theoretical justification and empirical evidence.
Novel probabilistic models forecast residential heating and electricity demand at hourly resolution.
problem Accurate hourly forecasting of residential heating and electricity demand.
method Probabilistic deep learning models trained on gas-heated region data.
result Significant improvement in forecast accuracy compared to NREL's ResStock model.
New method improves probabilistic electricity price predictions.
problem Improving point forecasts to probabilistic distributions for better decision-making.
method Isotonic Distributional Regression combined with other postprocessing methods.
result Isotonic Distributional Regression outperforms other methods in combining probabilistic distributions.
Due to the liberalization of markets, the change in the energy mix and the surrounding energy laws, electricity research is a dynamically altering field with steadily changing challenges. One challenge especially for investment decisions is to provide reliable short to mid-term forecasts despite high variation in the t…
Paper proposes a method for predicting any quantile of short-term electricity demand.
problem Uncertainty in power systems due to multiple factors.
method Proposes a novel general approach for distributional forecasting of short-term electricity demand.
result Demonstrates state-of-the-art distributional forecasting results for short-term electricity demand.
Paper optimizes demand aggregation for low-level electricity markets.
problem Accurate short-term load forecasting at low aggregation levels for market participants.
method Probabilistic portfolio optimization of residential households' demand using ARMA-GARCH models or KDE forecasts.
result Seasonal Residual approach outperforms others in accuracy and efficiency.
Our paper aims to model and forecast the electricity price by taking a completely new perspective on the data. It will be the first approach which is able to combine the insights of market structure models with extensive and modern econometric analysis. Instead of directly modeling the electricity price as it is usuall…
Paper improves probabilistic forecasts of electricity prices.
problem Combining multivariate probabilistic forecasts with dependencies.
method Online learning algorithm using Bernstein Online Aggregation (BOA).
result Significant improvement in CRPS over uniform combination.
Study predicts electricity prices using LSTM models with feature selection, considering market coupling.
problem Accurate day-ahead electricity price forecasting in coupled markets.
method Hybrid LSTM-based deep learning models with feature selection algorithms.
result Proposed models achieve considerably accurate results in Nordic market.
With developing of computation tools in the last years, data analysis methods to find insightful information are becoming more common among industries and researchers. This paper is the first part of the times series analysis of New England electricity price and demand to find anomaly in the data. In this paper time-se…
AutoML improves electricity demand forecasting models.
problem Optimizing GAM and state-space model parameters for short-term forecasting.
method Automated online generalized additive model selection using DRAGON package.
result The approach enhances predictive performance of adaptive models.
New online method for multivariate probabilistic electricity price forecasting.
problem Multivariate probabilistic forecasting of electricity prices.
method Online multivariate distributional regression with LASSO regularization.
result Robust and interpretable joint prediction intervals for 24-hour electricity prices.
Novel neural network predicts electricity prices with higher moments.
problem Probabilistic forecasting of volatile electricity prices.
method Distributional neural network with a probability layer.
result Significantly outperforms benchmarks in forecasting.
Study forecasts monthly electricity demand using pattern similarity-based methods.
problem Forecasting monthly electricity demand accurately.
method Pattern similarity-based forecasting methods (PSFMs) including k-NN, fuzzy, kernel regression, and GRNN.
result Ensemble models outperform individual PSFMs in forecasting accuracy.
The increasing penetration level of energy generation from renewable sources is demanding for more accurate and reliable forecasting tools to support classic power grid operations (e.g., unit commitment, electricity market clearing or maintenance planning). For this purpose, many physical models have been employed, and…
In this paper, we formulate a method for minimising the expectation value of the procurement cost of electricity in two popular spot markets: {\it day-ahead} and {\it intra-day}, under the assumption that expectation value of unit prices and the distributions of prediction errors for the electricity demand traded in tw…
The study improves load forecasting for electricity consumers using advanced machine learning models.
problem Improving short-term load forecasting for effective scheduling and decision-making.
method Proposes and evaluates statistical nonlinear models, including LSTM and GRU, for 15-min frequency electricity load forecasting.
result Advanced models outperform other models in out-of-sample forecasting accuracy, as shown by the Diebold-Mariano test.
New method selects recent similar periods for better electricity price forecasting.
problem Improving accuracy in forecasting electricity prices.
method Change-point detection (NOT method) to select calibration periods; estimating autoregressive models only for selected data.
result Significant improvement in forecasting accuracy compared to existing methods.
This review explores probabilistic forecasting methods in evolving energy markets.
problem Volatility and uncertainty in renewable energy markets require probabilistic forecasting for risk assessment.
method Traces evolution from Bayesian and distribution-based approaches to conformal prediction.
result Probabilistic forecasting offers a more comprehensive approach to risk assessment and market participation.
Research forecasts electricity spot prices using stochastic volatility models.
problem Forecasting day-ahead electricity prices in a spot market.
method Exploring and enriching a baseline stochastic volatility model with exogenous regressors.
result A better fitting model confirmed by out-of-sample forecasts.
The paper forecasts joint electricity demand across 14 British regions using additive models.
problem Forecasting regional electricity demand with cross-regional dependencies.
method Modified Cholesky parametrisation for multivariate Gaussian model, gradient boosting for model selection.
result The proposed model outperforms non-Gaussian copula-based models in forecasting.
In this paper we present a regression based model for day-ahead electricity spot prices. We estimate the considered linear regression model by the lasso estimation method. The lasso approach allows for many possible parameters in the model, but also shrinks and sparsifies the parameters automatically to avoid overfitti…
SQR Averaging improves probabilistic electricity price forecasting.
problem Accurate short-term price forecasting in electricity markets.
method Smoothing Quantile Regression Averaging.
result SQR Averaging leads to profit increases of up to 3.5% in day-ahead power trading.
Novel time series forecasting method using sliding window signatures.
problem Challenges in forecasting nonlinear and delayed time series data.
method Ridge regression with signature features calculated on sliding windows.
result Signature features effectively encode temporal and nonlinear dependencies, leading to accurate forecasts.
This research improves LSTM for monthly electricity demand forecasting using pattern-based methods.
problem Forecasting mid-term monthly electricity demand with high accuracy.
method Developed a hybrid LSTM model using x-patterns and exponential smoothing.
result The hybrid model outperformed standard LSTM and classical models.
This paper evaluates forecast quality in electricity markets beyond traditional accuracy measures.
problem Traditional accuracy measures fail to reflect the economic value of electricity price forecasts.
method Investigates four quality dimensions: accuracy, dispersion, association, and extremum identification.
result Dispersion- and association-based measures better capture forecast economic value.
Gas demand forecasting is a critical task for energy providers as it impacts on pipe reservation and stock planning. In this paper, the one-day-ahead forecasting of residential gas demand at country level is investigated by implementing and comparing five models: Ridge Regression, Gaussian Process (GP), k-Nearest Neigh…
New method for disaggregate electricity demand forecasting at household level.
problem Challenges in forecasting electricity demand at individual household level.
method Additive stacking method for probabilistic disaggregate electricity demand forecasting.
result Improved accuracy in disaggregate electricity demand forecasting.