Load Forecasting
37 papers with code • 0 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Load Forecasting
ASHRAE energy prediction III
PJM(AEP)
Latest papers with no code
Short-term power load forecasting method based on CNN-SAEDN-Res
In this method, feature extraction module is composed of a two-dimensional convolutional neural network, which is used to mine the local correlation between data and obtain high-dimensional data features.
Probabilistic load forecasting with Reservoir Computing
For this reason, point forecasts are not enough hence it is necessary to adopt methods that provide an uncertainty quantification.
An Error Correction Mid-term Electricity Load Forecasting Model Based on Seasonal Decomposition
Then, based on the idea of stacking ensemble, long short-term memory is employed as an error correction module to forecast the components separately, and the forecast results are treated as new features to be fed into extreme gradient boosting for the second-step forecasting.
SaDI: A Self-adaptive Decomposed Interpretable Framework for Electric Load Forecasting under Extreme Events
In this paper, we propose a novel forecasting framework, named Self-adaptive Decomposed Interpretable framework~(SaDI), which ensembles long-term trend, short-term trend, and period modelings to capture temporal characteristics in different components.
Interval Load Forecasting for Individual Households in the Presence of Electric Vehicle Charging
Results show that the proposed LSTM-BNNs achieve accuracy similar to point forecasts with the advantage of prediction intervals.
DiffLoad: Uncertainty Quantification in Load Forecasting with Diffusion Model
The uncertainties in load forecasting can be divided into two types: epistemic uncertainty and aleatoric uncertainty.
Meta-Regression Analysis of Errors in Short-Term Electricity Load Forecasting
In this article, we therefore present a Meta-Regression Analysis (MRA) that examines factors that influence the accuracy of short-term electricity load forecasts.
Leveraging Predictions in Power System Frequency Control: an Adaptive Approach
Ensuring the frequency stability of electric grids with increasing renewable resources is a key problem in power system operations.
Short-Term Electricity Load Forecasting Using the Temporal Fusion Transformer: Effect of Grid Hierarchies and Data Sources
In addition, the TFT appears to offer remarkable improvements over the LSTM approach for week-ahead forecasting (yielding a predictive error of 2. 52% (MAPE) at the lowest).
DA-LSTM: A Dynamic Drift-Adaptive Learning Framework for Interval Load Forecasting with LSTM Networks
Load forecasting is a crucial topic in energy management systems (EMS) due to its vital role in optimizing energy scheduling and enabling more flexible and intelligent power grid systems.
