Deep Learning in Renewable Energy Forecasting: A Cross-Dataset Evaluation of Temporal and Spatial Models

• URL: http://arxiv.org/abs/2505.03109v1
• Date: Tue, 06 May 2025 02:05:19 GMT
• Title: Deep Learning in Renewable Energy Forecasting: A Cross-Dataset Evaluation of Temporal and Spatial Models
• Authors: Lutfu Sua, Haibo Wang, Jun Huang,
• Abstract summary: This study deploys regularization approaches, including early stopping, neuron dropping, and L2 regularization, to reduce the overfitting problem associated with DL models.<n>The LSTM and models show superior performance. Their validation data exhibit exceptionally low root mean square error values.
• Score: 7.286091036139208
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Unpredictability of renewable energy sources coupled with the complexity of those methods used for various purposes in this area calls for the development of robust methods such as DL models within the renewable energy domain. Given the nonlinear relationships among variables in renewable energy datasets, DL models are preferred over traditional machine learning (ML) models because they can effectively capture and model complex interactions between variables. This research aims to identify the factors responsible for the accuracy of DL techniques, such as sampling, stationarity, linearity, and hyperparameter optimization for different algorithms. The proposed DL framework compares various methods and alternative training/test ratios. Seven ML methods, such as Long-Short Term Memory (LSTM), Stacked LSTM, Convolutional Neural Network (CNN), CNN-LSTM, Deep Neural Network (DNN), Multilayer Perceptron (MLP), and Encoder-Decoder (ED), were evaluated on two different datasets. The first dataset contains the weather and power generation data. It encompasses two distinct datasets, hourly energy demand data and hourly weather data in Spain, while the second dataset includes power output generated by the photovoltaic panels at 12 locations. This study deploys regularization approaches, including early stopping, neuron dropping, and L2 regularization, to reduce the overfitting problem associated with DL models. The LSTM and MLP models show superior performance. Their validation data exhibit exceptionally low root mean square error values.

Related papers

• Renewable Energy Prediction: A Comparative Study of Deep Learning Models for Complex Dataset Analysis [6.857122511301731]
  Deep learning (DL) models capture complex, nonlinear relationships in renewable energy datasets.<n>Seven machine learning methods, LSTM, Stacked LSTM, CNN, CNN-LSTM, Time-DistributedDistributed (TD-MLP) and Autoencoder (AE) are evaluated.<n>Regularization techniques such as early stopping, neuron dropout, L1 and L2 regularization are applied to address overfitting.
  arXiv  Detail & Related papers  (2025-01-27T02:10:10Z)
• Can AI be enabled to dynamical downscaling? A Latent Diffusion Model to mimic km-scale COSMO5.0\_CLM9 simulations [0.0]
  Downscaling techniques are one of the most prominent applications of Deep Learning (DL) in Earth System Modeling. In this study, we apply a Latent Diffusion Model (LDM) to downscale ERA5 data over Italy up to a resolution of 2 km. Our goal is to demonstrate that recent advancements in generative modeling enable DL to deliver results comparable to those of numerical dynamical models.
  arXiv  Detail & Related papers  (2024-06-19T15:20:28Z)
• Latent Semantic Consensus For Deterministic Geometric Model Fitting [109.44565542031384]
  We propose an effective method called Latent Semantic Consensus (LSC) LSC formulates the model fitting problem into two latent semantic spaces based on data points and model hypotheses. LSC is able to provide consistent and reliable solutions within only a few milliseconds for general multi-structural model fitting.
  arXiv  Detail & Related papers  (2024-03-11T05:35:38Z)
• Diffusion-Based Neural Network Weights Generation [80.89706112736353]
  D2NWG is a diffusion-based neural network weights generation technique that efficiently produces high-performing weights for transfer learning. Our method extends generative hyper-representation learning to recast the latent diffusion paradigm for neural network weights generation. Our approach is scalable to large architectures such as large language models (LLMs), overcoming the limitations of current parameter generation techniques.
  arXiv  Detail & Related papers  (2024-02-28T08:34:23Z)
• To Repeat or Not To Repeat: Insights from Scaling LLM under Token-Crisis [50.31589712761807]
  Large language models (LLMs) are notoriously token-hungry during pre-training, and high-quality text data on the web is approaching its scaling limit for LLMs. We investigate the consequences of repeating pre-training data, revealing that the model is susceptible to overfitting. Second, we examine the key factors contributing to multi-epoch degradation, finding that significant factors include dataset size, model parameters, and training objectives.
  arXiv  Detail & Related papers  (2023-05-22T17:02:15Z)
• Bayesian Neural Network Language Modeling for Speech Recognition [59.681758762712754]
  State-of-the-art neural network language models (NNLMs) represented by long short term memory recurrent neural networks (LSTM-RNNs) and Transformers are becoming highly complex. In this paper, an overarching full Bayesian learning framework is proposed to account for the underlying uncertainty in LSTM-RNN and Transformer LMs.
  arXiv  Detail & Related papers  (2022-08-28T17:50:19Z)
• A data filling methodology for time series based on CNN and (Bi)LSTM neural networks [0.0]
  We develop two Deep Learning models aimed at filling data gaps in time series obtained from monitored apartments in Bolzano, Italy. Our approach manages to capture the fluctuating nature of the data and shows good accuracy in reconstructing the target time series.
  arXiv  Detail & Related papers  (2022-04-21T09:40:30Z)
• Accurate Discharge Coefficient Prediction of Streamlined Weirs by Coupling Linear Regression and Deep Convolutional Gated Recurrent Unit [2.4475596711637433]
  The present study proposes data-driven modeling techniques, as an alternative to CFD simulation, to predict the discharge coefficient based on an experimental dataset. It is found that the proposed three layer hierarchical DL algorithm consists of a convolutional layer coupled with two subsequent GRU levels, which is also hybridized with the LR method, leads to lower error metrics.
  arXiv  Detail & Related papers  (2022-04-12T01:59:36Z)
• Dynamically-Scaled Deep Canonical Correlation Analysis [77.34726150561087]
  Canonical Correlation Analysis (CCA) is a method for feature extraction of two views by finding maximally correlated linear projections of them. We introduce a novel dynamic scaling method for training an input-dependent canonical correlation model.
  arXiv  Detail & Related papers  (2022-03-23T12:52:49Z)
• ES-dRNN: A Hybrid Exponential Smoothing and Dilated Recurrent Neural Network Model for Short-Term Load Forecasting [1.4502611532302039]
  Short-term load forecasting (STLF) is challenging due to complex time series (TS) This paper proposes a novel hybrid hierarchical deep learning model that deals with multiple seasonality. It combines exponential smoothing (ES) and a recurrent neural network (RNN)
  arXiv  Detail & Related papers  (2021-12-05T19:38:42Z)
• Convolutional Tensor-Train LSTM for Spatio-temporal Learning [116.24172387469994]
  We propose a higher-order LSTM model that can efficiently learn long-term correlations in the video sequence. This is accomplished through a novel tensor train module that performs prediction by combining convolutional features across time. Our results achieve state-of-the-art performance-art in a wide range of applications and datasets.
  arXiv  Detail & Related papers  (2020-02-21T05:00:01Z)

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.