Load Forecasting
36 papers with code • 0 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Load Forecasting
ASHRAE energy prediction III
PJM(AEP)
Most implemented papers
Short-Term Load Forecasting using Bi-directional Sequential Models and Feature Engineering for Small Datasets
Electricity load forecasting enables the grid operators to optimally implement the smart grid's most essential features such as demand response and energy efficiency.
Probabilistic Load Forecasting Based on Adaptive Online Learning
Conventional load forecasting techniques obtain single-value load forecasts by exploiting consumption patterns of past load demand.
Data-Driven Copy-Paste Imputation for Energy Time Series
The CPI method copies data blocks with similar properties and pastes them into gaps of the time series while preserving the total energy of each gap.
Review of Low Voltage Load Forecasting: Methods, Applications, and Recommendations
The increased digitalisation and monitoring of the energy system opens up numerous opportunities to decarbonise the energy system.
Hierarchical transfer learning with applications for electricity load forecasting
The recent abundance of data on electricity consumption at different scales opens new challenges and highlights the need for new techniques to leverage information present at finer scales in order to improve forecasts at wider scales.
ES-dRNN: A Hybrid Exponential Smoothing and Dilated Recurrent Neural Network Model for Short-Term Load Forecasting
A multi-layer RNN is equipped with a new type of dilated recurrent cell designed to efficiently model both short and long-term dependencies in TS.
Methodology for forecasting and optimization in IEEE-CIS 3rd Technical Challenge
For the forecast, I used a quantile regression forest approach using the solar variables provided by the Bureau of Meterology of Australia (BOM) and many of the weather variables from the European Centre for Medium-Range Weather Forecasting (ECMWF).
ES-dRNN with Dynamic Attention for Short-Term Load Forecasting
Short-term load forecasting (STLF) is a challenging problem due to the complex nature of the time series expressing multiple seasonality and varying variance.
Optimal Adaptive Prediction Intervals for Electricity Load Forecasting in Distribution Systems via Reinforcement Learning
Case studies on both load and net load demonstrate that the proposed method can better adapt to data distribution compared with online central PIs method.
Contextually Enhanced ES-dRNN with Dynamic Attention for Short-Term Load Forecasting
slaweks17/es-adrnn-with-context
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These cells enable the model to capture short-term, long-term and seasonal dependencies across time series as well as to weight dynamically the input information.
