Automated Deep Learning for Load Forecasting

• URL: http://arxiv.org/abs/2405.08842v1
• Date: Tue, 14 May 2024 07:51:55 GMT
• Title: Automated Deep Learning for Load Forecasting
• Authors: Julie Keisler, Sandra Claudel, Gilles Cabriel, Margaux Brégère,
• Abstract summary: This paper explains why and how we used Automated Deep Learning (AutoDL) to find performing Deep Neural Networks (DNNs) for load forecasting. We end up creating an AutoDL framework called EnergyDragon by extending the DRAGON package and applying it to load forecasting. We demonstrate on the French load signal that EnergyDragon can find original DNNs that outperform state-of-the-art load forecasting methods.
• Score: 0.34952465649465553
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate forecasting of electricity consumption is essential to ensure the performance and stability of the grid, especially as the use of renewable energy increases. Forecasting electricity is challenging because it depends on many external factors, such as weather and calendar variables. While regression-based models are currently effective, the emergence of new explanatory variables and the need to refine the temporality of the signals to be forecasted is encouraging the exploration of novel methodologies, in particular deep learning models. However, Deep Neural Networks (DNNs) struggle with this task due to the lack of data points and the different types of explanatory variables (e.g. integer, float, or categorical). In this paper, we explain why and how we used Automated Deep Learning (AutoDL) to find performing DNNs for load forecasting. We ended up creating an AutoDL framework called EnergyDragon by extending the DRAGON package and applying it to load forecasting. EnergyDragon automatically selects the features embedded in the DNN training in an innovative way and optimizes the architecture and the hyperparameters of the networks. We demonstrate on the French load signal that EnergyDragon can find original DNNs that outperform state-of-the-art load forecasting methods as well as other AutoDL approaches.

Related papers

• Gradient Transformation: Towards Efficient and Model-Agnostic Unlearning for Dynamic Graph Neural Networks [66.70786325911124]
  Graph unlearning has emerged as an essential tool for safeguarding user privacy and mitigating the negative impacts of undesirable data. With the increasing prevalence of DGNNs, it becomes imperative to investigate the implementation of dynamic graph unlearning. We propose an effective, efficient, model-agnostic, and post-processing method to implement DGNN unlearning.
  arXiv  Detail & Related papers  (2024-05-23T10:26:18Z)
• Secure short-term load forecasting for smart grids with transformer-based federated learning [0.0]
  Electricity load forecasting is an essential task within smart grids to assist demand and supply balance. Fine-grained load profiles can expose users' electricity consumption behaviors, which raises privacy and security concerns. This paper presents a novel transformer-based deep learning approach with federated learning for short-term electricity load prediction.
  arXiv  Detail & Related papers  (2023-10-26T15:27:55Z)
• Online Evolutionary Neural Architecture Search for Multivariate Non-Stationary Time Series Forecasting [72.89994745876086]
  This work presents the Online Neuro-Evolution-based Neural Architecture Search (ONE-NAS) algorithm. ONE-NAS is a novel neural architecture search method capable of automatically designing and dynamically training recurrent neural networks (RNNs) for online forecasting tasks. Results demonstrate that ONE-NAS outperforms traditional statistical time series forecasting methods.
  arXiv  Detail & Related papers  (2023-02-20T22:25:47Z)
• ConCerNet: A Contrastive Learning Based Framework for Automated Conservation Law Discovery and Trustworthy Dynamical System Prediction [82.81767856234956]
  This paper proposes a new learning framework named ConCerNet to improve the trustworthiness of the DNN based dynamics modeling. We show that our method consistently outperforms the baseline neural networks in both coordinate error and conservation metrics.
  arXiv  Detail & Related papers  (2023-02-11T21:07:30Z)
• Tree-Based Learning in RNNs for Power Consumption Forecasting [0.4822598110892847]
  A Recurrent Neural Network that operates on several time lags, called an RNN(p), is the natural generalization of an Autoregressive ARX(p) model. We prove that, when training RNN(p) models, other learning algorithms turn out to be much more efficient in terms of both time and space complexity. We present an application of RNN(p) models for power consumption forecasting on the hourly scale.
  arXiv  Detail & Related papers  (2022-09-03T09:21:39Z)
• Toward Dynamic Stability Assessment of Power Grid Topologies using Graph Neural Networks [0.0]
  Renewables introduce new challenges to power grids regarding the dynamic stability due to decentralization, reduced inertia, and volatility in production. graph neural networks (GNNs) are a promising method to reduce the computational effort of analyzing the dynamic stability of power grids. GNNs are surprisingly effective at predicting the highly non-linear targets from topological information only.
  arXiv  Detail & Related papers  (2022-06-10T07:23:22Z)
• Pretraining Graph Neural Networks for few-shot Analog Circuit Modeling and Design [68.1682448368636]
  We present a supervised pretraining approach to learn circuit representations that can be adapted to new unseen topologies or unseen prediction tasks. To cope with the variable topological structure of different circuits we describe each circuit as a graph and use graph neural networks (GNNs) to learn node embeddings. We show that pretraining GNNs on prediction of output node voltages can encourage learning representations that can be adapted to new unseen topologies or prediction of new circuit level properties.
  arXiv  Detail & Related papers  (2022-03-29T21:18:47Z)
• Random vector functional link neural network based ensemble deep learning for short-term load forecasting [14.184042046855884]
  This paper proposes a novel ensemble deep Random Functional Link (edRVFL) network for electricity load forecasting. The hidden layers are stacked to enforce deep representation learning. The model generates the forecasts by ensembling the outputs of each layer.
  arXiv  Detail & Related papers  (2021-07-30T01:20:48Z)
• Physics-Informed Graphical Neural Network for Parameter & State Estimations in Power Systems [4.416484585765027]
  We present a hybrid scheme which embeds physics modeling of power systems into Graphical Neural Networks (GNN) We build a physics-informed method, named Power-GNN, which reconstructs physical, thus interpretable, parameters within Effective Power Flow (EPF) models. In our experiments, we test the Power-GNN on different realistic power networks, including these with thousands of loads and hundreds of generators.
  arXiv  Detail & Related papers  (2021-02-12T04:32:50Z)
• Learning to Continuously Optimize Wireless Resource In Episodically Dynamic Environment [55.91291559442884]
  This work develops a methodology that enables data-driven methods to continuously learn and optimize in a dynamic environment. We propose to build the notion of continual learning into the modeling process of learning wireless systems. Our design is based on a novel min-max formulation which ensures certain fairness" across different data samples.
  arXiv  Detail & Related papers  (2020-11-16T08:24:34Z)
• Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration [130.89746032163106]
  We propose ALOE, a new algorithm for learning conditional and unconditional EBMs for discrete structured data. We show that the energy function and sampler can be trained efficiently via a new variational form of power iteration. We present an energy model guided fuzzer for software testing that achieves comparable performance to well engineered fuzzing engines like libfuzzer.
  arXiv  Detail & Related papers  (2020-11-10T19:31:29Z)

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.