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Abstract Optimal transport (OT) methods seek a transformation map (or plan) between two probability measures, such that the transformation has the minimum transportation cost. Such a minimum transport cost, with a certain power transform, is called the Wasserstein distance. Recently, OT methods have drawn great attention in statistics, machine learning, and computer science, especially in deep generative neural networks. Despite its broad applications, the estimation of high‐dimensional Wasserstein distances is a well‐known challenging problem owing to the curse‐of‐dimensionality. There are some cutting‐edge projection‐based techniques that tackle high‐dimensional OT problems. Three major approaches of such techniques are introduced, respectively, the slicing approach, the iterative projection approach, and the projection robust OT approach. Open challenges are discussed at the end of the review. This article is categorized under:Statistical and Graphical Methods of Data Analysis > Dimension ReductionStatistical Learning and Exploratory Methods of the Data Sciences > Manifold Learning
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This content will become publicly available on September 1, 2026
Statistical and Machine Learning Approaches for Electrical Energy Forecasting
ABSTRACT With renewable energy being aggressively integrated into the grid, energy supplies are becoming vulnerable to weather and the environment, and are often incapable of meeting population demands at a large scale if not accurately predicted for energy planning. Understanding consumers' power demands ahead of time and the influences of weather on consumption and generation can help producers generate effective power management plans to support the target demand. In addition to the high correlation with the environment, consumers' behaviors also cause non‐stationary characteristics of energy data, which is the main challenge for energy prediction. In this survey, we perform a review of the literature on prediction methods in the energy field. So far, most of the available research encompasses one type of generation or consumption. There is no research approaching prediction in the energy sector as a whole and its correlated features. We propose to address the energy prediction challenges from both consumption and generation sides, encompassing techniques from statistical to machine learning techniques. We also summarize the work related to energy prediction, electricity measurements, challenges related to energy consumption and generation, energy forecasting methods, and real‐world energy forecasting resources, such as datasets and software solutions for energy prediction. This article is categorized under:Application Areas > Industry Specific ApplicationsTechnologies > PredictionTechnologies > Machine Learning
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- PAR ID:
- 10616082
- Publisher / Repository:
- WIREs Data Mining and Knowledge Discovery
- Date Published:
- Journal Name:
- WIREs Data Mining and Knowledge Discovery
- Volume:
- 15
- Issue:
- 3
- ISSN:
- 1942-4787
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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