2018/12/01 by Jonathan Dumas, Dumas, Jonathan, Bertrand Cornélusse +1
Computer Science · Decision Sciences · Engineering · Mathematics · #Energy Load and Power Forecasting #FOS: Computer and information sciences #Forecasting Techniques and Applications #Grey System Theory Applications #Information Retrieval (cs.IR) #Machine Fault Diagnosis Techniques #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Non-Destructive Testing Techniques #cs.IR #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1901.05052
openalex publication_date 2018/12/01 · arxiv created 2020/03/18 · arxiv updated 2020/03/19 · openalex created_date 2022/08/01 · openalex updated_date 2026/07/28
The key contribution of this paper is to propose a classification into two dimensions of the load forecasting studies to decide which forecasting tools to use in which case. This classification aims to provide a synthetic view of the relevant forecasting techniques and methodologies by forecasting problem. In addition, the key principles of the main techniques and methodologies used are summarized along with the reviews of these papers. The classification process relies on two couples of parameters that define a forecasting problem. Each article is classified with key information about the dataset used and the forecasting tools implemented: the forecasting techniques (probabilistic or deterministic) and methodologies, the data cleansing techniques, and the error metrics. The process to select the articles reviewed in this paper was conducted into two steps. First, a set of load forecasting studies was built based on relevant load forecasting reviews and forecasting competitions. The second step consisted in selecting the most relevant studies of this set based on the following criteria: the quality of the description of the forecasting techniques and methodologies implemented, the description of the results, and the contributions. This paper can be read in two passes. The first one by identifying the forecasting problem of interest to select the corresponding class into one of the four classification tables. Each one references all the articles classified across a forecasting horizon. They provide a synthetic view of the forecasting tools used by articles addressing similar forecasting problems. Then, a second level composed of four Tables summarizes key information about the forecasting tools and the results of these studies. The second pass consists in reading the key principles of the main techniques and methodologies of interest and the reviews of the articles.