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The key contribution of this paper is to propose a classification into twodimensions of the load forecasting studies to decide which forecasting tools touse in which case. This classification aims to provide a synthetic view of therelevant forecasting techniques and methodologies by forecasting problem. Inaddition, the key principles of the main techniques and methodologies used aresummarized along with the reviews of these papers.  The classification process relies on two couples of parameters that define aforecasting problem. Each article is classified with key information about thedataset used and the forecasting tools implemented: the forecasting techniques(probabilistic or deterministic) and methodologies, the data cleansingtechniques, and the error metrics.  The process to select the articles reviewed in this paper was conducted intotwo steps. First, a set of load forecasting studies was built based on relevantload forecasting reviews and forecasting competitions. The second stepconsisted in selecting the most relevant studies of this set based on thefollowing criteria: the quality of the description of the forecastingtechniques and methodologies implemented, the description of the results, andthe contributions.  This paper can be read in two passes. The first one by identifying theforecasting problem of interest to select the corresponding class into one ofthe four classification tables. Each one references all the articles classifiedacross a forecasting horizon. They provide a synthetic view of the forecastingtools used by articles addressing similar forecasting problems. Then, a secondlevel composed of four Tables summarizes key information about the forecastingtools and the results of these studies. The second pass consists in reading thekey principles of the main techniques and methodologies of interest and thereviews of the articles.

Classification of load forecasting studies by forecasting problem to select load forecasting techniques and methodologies