A generalised multi-factor deep learning electricity load forecasting model for wildfire-prone areas

• URL: http://arxiv.org/abs/2304.10686v1
• Date: Fri, 21 Apr 2023 00:42:24 GMT
• Title: A generalised multi-factor deep learning electricity load forecasting model for wildfire-prone areas
• Authors: Weijia Yang, Sarah N. Sparrow, David C.H. Wallom
• Abstract summary: This paper proposes a generalised and robust multi-factor Gated Recurrent Unit (GRU) based Deep Learning (DL) model to forecast electricity load in distribution networks during wildfire seasons. Compared to the regular use of instantaneous temperature, the Mean Absolute Percentage Error (MAPE) is decreased by 30.73%. The proposed method achieves an average forecast MAPE of around 3%, giving a potential annual energy saving of AU$80.46 million for the state of Victoria.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper proposes a generalised and robust multi-factor Gated Recurrent Unit (GRU) based Deep Learning (DL) model to forecast electricity load in distribution networks during wildfire seasons. The flexible modelling methods consider data input structure, calendar effects and correlation-based leading temperature conditions. Compared to the regular use of instantaneous temperature, the Mean Absolute Percentage Error (MAPE) is decreased by 30.73% by using the proposed input feature selection and leading temperature relationships. Our model is generalised and applied to eight real distribution networks in Victoria, Australia, during the wildfire seasons of 2015-2020. We demonstrate that the GRU-based model consistently outperforms another DL model, Long Short-Term Memory (LSTM), at every step, giving average improvements in Mean Squared Error (MSE) and MAPE of 10.06% and 12.86%, respectively. The sensitivity to large-scale climate variability in training data sets, e.g. El Ni\~no or La Ni\~na years, is considered to understand the possible consequences for load forecasting performance stability, showing minimal impact. Other factors such as regional poverty rate and large-scale off-peak electricity use are potential factors to further improve forecast performance. The proposed method achieves an average forecast MAPE of around 3%, giving a potential annual energy saving of AU\$80.46 million for the state of Victoria.

Related papers

• China Regional 3km Downscaling Based on Residual Corrective Diffusion Model [39.12803910865843]
  This work focuses on statistical downscaling, which establishes statistical relationships between low-resolution and high-resolution historical data.<n>In contrast to the original work of CorrDiff, the region considered in this work is nearly 40 times larger.<n>Deep learning has emerged as a powerful tool for this task, giving rise to various high-performance super-resolution models.
  arXiv  Detail & Related papers  (2025-12-05T02:27:08Z)
• Attention-Enhanced LSTM Modeling for Improved Temperature and Rainfall Forecasting in Bangladesh [0.0]
  Existing models often struggle to capture long-range dependencies and complex temporal patterns in climate data.<n>This study introduces an advanced Long Short-Term Memory (LSTM) model integrated with an attention mechanism to enhance the prediction of temperature and rainfall dynamics.
  arXiv  Detail & Related papers  (2025-10-12T17:03:45Z)
• Leveraging External Factors in Household-Level Electrical Consumption Forecasting using Hypernetworks [15.77742422761257]
  We show that a hypernetwork architecture can leverage external factors to enhance the accuracy of global electrical consumption forecasting models.<n>We collected a comprehensive dataset spanning two years, comprising consumption data from over 6000 luxembourgish households.
  arXiv  Detail & Related papers  (2025-06-17T12:35:24Z)
• Machine Learning-based Regional Cooling Demand Prediction with Optimised Dataset Partitioning [7.745583292171836]
  Accurately predicting cooling demand in urban domestic buildings is essential for maintaining energy efficiency. This study introduces a generalised framework for developing high-resolution Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks.
  arXiv  Detail & Related papers  (2025-03-04T12:43:33Z)
• OneForecast: A Universal Framework for Global and Regional Weather Forecasting [67.61381313555091]
  We propose a global-regional nested weather forecasting framework (OneForecast) based on graph neural networks.<n>By combining a dynamic system perspective with multi-grid theory, we construct a multi-scale graph structure and densify the target region.<n>We introduce an adaptive messaging mechanism, using dynamic gating units, to deeply integrate node and edge features for more accurate extreme event forecasting.
  arXiv  Detail & Related papers  (2025-02-01T06:49:16Z)
• Machine learning-based probabilistic forecasting of solar irradiance in Chile [0.7067443325368975]
  This work investigates probabilistic forecasts of solar irradiance for Regions III and IV in Chile. We propose a neural network-based post-processing method resulting in improved 8-member ensemble predictions. All forecasts are evaluated against station observations for 30 locations, and the skill of post-processed predictions is compared to the raw WRF ensemble.
  arXiv  Detail & Related papers  (2024-11-17T13:22:05Z)
• Deep learning surrogate models of JULES-INFERNO for wildfire prediction on a global scale [10.16915256748265]
  Two data-driven models are built in this work based on Deep Learning techniques to surrogate the JULES-INFERNO model. More precisely, these machine learning models take global temperature, vegetation density, soil moisture and previous forecasts as inputs to predict the subsequent global area burnt. Results show a strong performance of the proposed models, in terms of both computational efficiency (less than 20 seconds for 30 years of prediction on a laptop CPU) and prediction accuracy (with AEP under 0.3% and SSIM over 98% compared to the outputs of JULES-INFERNO)
  arXiv  Detail & Related papers  (2024-08-30T20:05:00Z)
• Efficient mid-term forecasting of hourly electricity load using generalized additive models [0.0]
  We propose a novel forecasting method using Generalized Additive Models (GAMs) built from interpretable P-splines and enhanced with autoregressive post-processing. The proposed model is evaluated on load data from 24 European countries.
  arXiv  Detail & Related papers  (2024-05-27T11:41:41Z)
• Weather Prediction with Diffusion Guided by Realistic Forecast Processes [49.07556359513563]
  We introduce a novel method that applies diffusion models (DM) for weather forecasting. Our method can achieve both direct and iterative forecasting with the same modeling framework. The flexibility and controllability of our model empowers a more trustworthy DL system for the general weather community.
  arXiv  Detail & Related papers  (2024-02-06T21:28:42Z)
• ExtremeCast: Boosting Extreme Value Prediction for Global Weather Forecast [57.6987191099507]
  We introduce Exloss, a novel loss function that performs asymmetric optimization and highlights extreme values to obtain accurate extreme weather forecast. We also introduce ExBooster, which captures the uncertainty in prediction outcomes by employing multiple random samples. Our solution can achieve state-of-the-art performance in extreme weather prediction, while maintaining the overall forecast accuracy comparable to the top medium-range forecast models.
  arXiv  Detail & Related papers  (2024-02-02T10:34:13Z)
• FengWu-GHR: Learning the Kilometer-scale Medium-range Global Weather Forecasting [56.73502043159699]
  This work presents FengWu-GHR, the first data-driven global weather forecasting model running at the 0.09$circ$ horizontal resolution. It introduces a novel approach that opens the door for operating ML-based high-resolution forecasts by inheriting prior knowledge from a low-resolution model. The hindcast of weather prediction in 2022 indicates that FengWu-GHR is superior to the IFS-HRES.
  arXiv  Detail & Related papers  (2024-01-28T13:23:25Z)
• Residual Corrective Diffusion Modeling for Km-scale Atmospheric Downscaling [58.456404022536425]
  State of the art for physical hazard prediction from weather and climate requires expensive km-scale numerical simulations driven by coarser resolution global inputs. Here, a generative diffusion architecture is explored for downscaling such global inputs to km-scale, as a cost-effective machine learning alternative. The model is trained to predict 2km data from a regional weather model over Taiwan, conditioned on a 25km global reanalysis.
  arXiv  Detail & Related papers  (2023-09-24T19:57:22Z)
• A Meta-Learning Approach to Predicting Performance and Data Requirements [163.4412093478316]
  We propose an approach to estimate the number of samples required for a model to reach a target performance. We find that the power law, the de facto principle to estimate model performance, leads to large error when using a small dataset. We introduce a novel piecewise power law (PPL) that handles the two data differently.
  arXiv  Detail & Related papers  (2023-03-02T21:48:22Z)
• A Hybrid Model for Forecasting Short-Term Electricity Demand [59.372588316558826]
  Currently the UK Electric market is guided by load (demand) forecasts published every thirty minutes by the regulator. We present HYENA: a hybrid predictive model that combines feature engineering (selection of the candidate predictor features), mobile-window predictors and LSTM encoder-decoders.
  arXiv  Detail & Related papers  (2022-05-20T22:13:25Z)
• Surrogate Ensemble Forecasting for Dynamic Climate Impact Models [0.0]
  This study considers a climate driven disease model, the Liverpool Malaria Model (LMM), which predicts the malaria transmission coefficient R0. The input and output data is used to train surrogate models in the form of a Random Forest Quantile Regression (RFQR) model and a Bayesian Long Short-Term Memory (BLSTM) neural network.
  arXiv  Detail & Related papers  (2022-04-12T13:30:01Z)
• Neural Network Middle-Term Probabilistic Forecasting of Daily Power Consumption [0.0]
  We propose a new modelling approach that incorporates trend, seasonality and weather conditions, as explicative variables in a shallow Neural Network with an autoregressive feature. We obtain excellent results for density forecast on the one-year test set applying it to the daily power consumption in New England U.S.A.
  arXiv  Detail & Related papers  (2020-06-05T13:07:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.