# Influences in Forecast Errors for Wind and Photovoltaic Power: A Study on Machine Learning Models

arXiv: https://arxiv.org/abs/1905.13668 · HTML: https://mlsanity.com/paper/1905.13668/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2019.

## Built on by (most cited first, 2 of 2)

- [Task Embedding Temporal Convolution Networks for Transfer Learning Problems in Renewable Power Time-Series Forecast](https://mlsanity.com/paper/2204.13908/) (2022)
- [Model Selection, Adaptation, and Combination for Transfer Learning in Wind and Photovoltaic Power Forecasts](https://mlsanity.com/paper/2204.13293/) (2022)

## Abstract (arXiv)

Despite the increasing importance of forecasts of renewable energy, current planning studies only address a general estimate of the forecast quality to be expected and selected forecast horizons. However, these estimates allow only a limited and highly uncertain use in the planning of electric power distribution. More reliable planning processes require considerably more information about future forecast quality. In this article, we present an in-depth analysis and comparison of influencing factors regarding uncertainty in wind and photovoltaic power forecasts, based on four different machine learning (ML) models. In our analysis, we found substantial differences in uncertainty depending on ML models, data coverage, and seasonal patterns that have to be considered in future planning studies.