Energy time series forecasting-Analytical and empirical assessment of conventional and machine learning models

• URL: http://arxiv.org/abs/2108.10663v1
• Date: Tue, 24 Aug 2021 12:02:26 GMT
• Title: Energy time series forecasting-Analytical and empirical assessment of conventional and machine learning models
• Authors: Hala Hamdoun, Alaa Sagheer and Hassan Youness
• Abstract summary: Machine learning methods have been adopted in the literature as contenders to conventional methods to solve the energy time series forecasting (TSF) problems. Yet, the evidence about their performance in to solve the energy TSF problems, in terms of accuracy and computational requirements, is scanty. This paper provides a comprehensive analytical assessment for conventional,machine learning, and deep learning methods that can be utilized to solve various energy TSF problems.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Machine learning methods have been adopted in the literature as contenders to conventional methods to solve the energy time series forecasting (TSF) problems. Recently, deep learning methods have been emerged in the artificial intelligence field attaining astonishing performance in a wide range of applications. Yet, the evidence about their performance in to solve the energy TSF problems, in terms of accuracy and computational requirements, is scanty. Most of the review articles that handle the energy TSF problem are systematic reviews, however, a qualitative and quantitative study for the energy TSF problem is not yet available in the literature. The purpose of this paper is twofold, first it provides a comprehensive analytical assessment for conventional,machine learning, and deep learning methods that can be utilized to solve various energy TSF problems. Second, the paper carries out an empirical assessment for many selected methods through three real-world datasets. These datasets related to electrical energy consumption problem, natural gas problem, and electric power consumption of an individual household problem.The first two problems are univariate TSF and the third problem is a multivariate TSF. Com-pared to both conventional and machine learning contenders, the deep learning methods attain a significant improvement in terms of accuracy and forecasting horizons examined. In the mean-time, their computational requirements are notably greater than other contenders. Eventually,the paper identifies a number of challenges, potential research directions, and recommendations to the research community may serve as a basis for further research in the energy forecasting domain.

Related papers

• Underwater Object Detection in the Era of Artificial Intelligence: Current, Challenge, and Future [119.88454942558485]
  Underwater object detection (UOD) aims to identify and localise objects in underwater images or videos. In recent years, artificial intelligence (AI) based methods, especially deep learning methods, have shown promising performance in UOD.
  arXiv  Detail & Related papers  (2024-10-08T00:25:33Z)
• Learning Iterative Reasoning through Energy Diffusion [90.24765095498392]
  We introduce iterative reasoning through energy diffusion (IRED), a novel framework for learning to reason for a variety of tasks. IRED learns energy functions to represent the constraints between input conditions and desired outputs. We show IRED outperforms existing methods in continuous-space reasoning, discrete-space reasoning, and planning tasks.
  arXiv  Detail & Related papers  (2024-06-17T03:36:47Z)
• Exploring Artificial Intelligence Methods for Energy Prediction in Healthcare Facilities: An In-Depth Extended Systematic Review [0.9208007322096533]
  This study conducted a literature review, utilizing the PRISMA framework, of articles that employed machine learning and artificial intelligence techniques for predicting energy consumption in hospital buildings. This review revealed a diverse range of data inputs influencing energy prediction, with occupancy and meteorological data emerging as significant predictors. The findings underscore the immense potential of AI in optimizing hospital energy consumption but also highlight the need for more comprehensive and granular research.
  arXiv  Detail & Related papers  (2023-11-27T13:30:20Z)
• Knowledge-enhanced Neural Machine Reasoning: A Review [67.51157900655207]
  We introduce a novel taxonomy that categorizes existing knowledge-enhanced methods into two primary categories and four subcategories. We elucidate the current application domains and provide insight into promising prospects for future research.
  arXiv  Detail & Related papers  (2023-02-04T04:54:30Z)
• Automated Extraction of Energy Systems Information from Remotely Sensed Data: A Review and Analysis [10.137044808866053]
  High quality energy systems information is a crucial input to energy systems research, modeling, and decision-making. Recently, remotely sensed data have emerged as a potentially rich source of energy systems information. Recent breakthroughs in machine learning have enabled automated and rapid extraction of useful information.
  arXiv  Detail & Related papers  (2022-02-18T14:38:49Z)
• Learning Physical Concepts in Cyber-Physical Systems: A Case Study [72.74318982275052]
  We provide an overview of the current state of research regarding methods for learning physical concepts in time series data. We also analyze the most important methods from the current state of the art using the example of a three-tank system.
  arXiv  Detail & Related papers  (2021-11-28T14:24:52Z)
• Machine learning methods for modelling and analysis of time series signals in geoinformatics [2.193013035690221]
  This dissertation evaluates the performance of several deep learning (DL) architectures on a large number of time series datasets of different nature and for different applications. The first problem is related to ionospheric Total Content (TEC) modeling which is an important issue in many real time Global Navigation System Satellites (GNSS) applications. The next problem is energy disaggregation which is an important issue for energy efficiency and energy consumption awareness.
  arXiv  Detail & Related papers  (2021-09-16T16:18:13Z)
• An Extensible Benchmark Suite for Learning to Simulate Physical Systems [60.249111272844374]
  We introduce a set of benchmark problems to take a step towards unified benchmarks and evaluation protocols. We propose four representative physical systems, as well as a collection of both widely used classical time-based and representative data-driven methods.
  arXiv  Detail & Related papers  (2021-08-09T17:39:09Z)
• Artificial Intelligence Based Prognostic Maintenance of Renewable Energy Systems: A Review of Techniques, Challenges, and Future Research Directions [3.1123064748686287]
  Data Analytics and Machine Learning (ML) techniques are being used to increase the overall efficiency of these prognostic maintenance systems. This paper provides an overview of the predictive/prognostic maintenance frameworks reported in the literature. Being a key aspect of ML-based solutions, we also discuss some of the commonly used publicly available datasets in the domain.
  arXiv  Detail & Related papers  (2021-04-20T11:41:00Z)
• Randomization-based Machine Learning in Renewable Energy Prediction Problems: Critical Literature Review, New Results and Perspectives [6.771141943827748]
  We review the most important characteristics of randomization-based machine learning approaches and their application to renewable energy prediction problems. We support our critical analysis with an extensive experimental study, comprising real-world problems related to solar, wind and hydro-power energy.
  arXiv  Detail & Related papers  (2021-03-26T17:38:46Z)
• Opportunities and Challenges of Deep Learning Methods for Electrocardiogram Data: A Systematic Review [62.490310870300746]
  The electrocardiogram (ECG) is one of the most commonly used diagnostic tools in medicine and healthcare. Deep learning methods have achieved promising results on predictive healthcare tasks using ECG signals. This paper presents a systematic review of deep learning methods for ECG data from both modeling and application perspectives.
  arXiv  Detail & Related papers  (2019-12-28T02:44:29Z)

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.