Highlights  A systematic review protocol provides unbiased and meaningful meta-information  A direct model accuracy comparison across studies is meaningless  A taxonomy for an informed forecasting model's selection is proposed  Recommendations on writing electrical load forecasting related paper are given ABSTRACT Electricity forecasting is an essential component of smart grid, which has attracted increasing academic interest. Forecasting enables informed and efficient responses for electricity demand. However, various forecasting models exist making it difficult for inexperienced researchers to make an informed model selection. This paper presents a systematic review of forecasting models with the main purpose of identifying which model is best suited for a particular case or scenario. Over 113 different case studies reported across 41 academic papers have been used for the comparison. The timeframe, inputs, outputs, scale, data sample

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