Abstract— These predictions show theneed for a

Abstract— The increase in energy demand prediction tech-niques will not only be beneficial to energy companies butalso to the environment. This paper will focus on methodsin literature used for short-term load forecasting. Short-termload forecasting attempts to predict energy demand given a setof variables such as temperature, wind speed and humidity.Throughout the decades methods have been improved andrefined with new machine learning techniques taking the placeof old statistical analytical methods. This review will focus onmethods used in literature focusing on the evolution of thealgorithms used. There has been considerable improvementbut still no concrete algorithm which performs best, as manydifferent factors can effect the results of the systems used.I. INTRODUCTIONThe rise of competitive energy markets and the increasingthreat from global warming has increased the need foraccurate robust energy load prediction models. There aremany types of energy load prediction from short term predic-tions which vary from minutes to days in advance, to longterm predictions which can attempt to predict energy loadestimates up to years in advance. These long term estimatescan be used as an indicator to energy companies in how toinvest in future infrastructure. Though it is not necessarilyimplied that a larger lead-time leads to a higher forecastingerror 1. In this review short term load forecasting will bethe main focus. Generally when forecasting energy load theMAPE (mean absolute percentage error) is usedwhere A n is the actual value and F n is the forecast value.The reason for this is that if the MPE (mean percentage error)was used the average error could be much lower due to overand under estimates canceling each other out.where A n is the actual value and F n is the forecast value.In 1984, from a survey of the UK power system itwas concluded that an increase in forecasting error of 1%would cost around £10 million more in operating costs 2.This prediction is now 33 years old with more modernestimates taking its place.34. These predictions show theneed for a robust and efficient method for load forecasting.There has been an explosion in artificial intelligenceapplications in the energy sector in recent decades. The useof algorithms that are largely non-linear and can adapt tosuit multiple types of problems are vital for energy systemsto predict load and flow. These systems can be used onlong to short-term load forecasting problems each with theirrelative advantages and disadvantages.Initially modeling of STLF (short-term load forecasting)was attempted by using an ARIMA (auto-regressiveintegrated moving average) model which was popularizedby Box and Jenkins 5. ANN (artificial neural networks)were then initially used by Park et al 6 with some promisingresults which were built upon in following decades. Astechnology has advanced and more computationally intensivealgorithms have been created, different algorithms have beenused to solve this problem. These algorithms include, butare not limited to, PNARIMA (periodic non-linear ARIMA)7, SVM (support vector machines) 8, fuzzy logic 910,multiple regression techniques and models in combinationwith genetic algorithms 11 and PCA (principal componentanalysis) 12.In the first part of this review, STLF will be described alongwith the difficulties faced by researchers. Then well usedmodels will be discussed along with techniques of findingrelevant variables and the methods to separate them fromredundant variables. Relevant papers with state-of-the-artmethods shall then be discussed focusing on the reasonsbehind each model, showing their relative advantagesand disadvantages. Finally, the results will be stated andcritiqued with conclusions drawn from them.II. S HORT -T ERM L OAD F ORECASTINGShort-term load forecasting is the process of using modelsto predict energy load from minutes to days in advance. Ithas many economic and environmental advantages rangingfrom reducing unnecessary cost to reducing the creation ofbi-products from energy production which can damage theenvironment.Much literature states that there is a noticeable differencein load on Saturday, Sunday and Monday 13. These dayscoincide with a Christian weekday whereas in countrieswhich practice other religions, these increased energyconsumption days can be seen on different days of theweek. This behavior is seen in Iranian datasets 14 whosedominant religion is Islam.Energy companies usually provide energy to differenttypes of customers. These range from industrial, commercialand residential with each having different respective loadcurves. Figure 1 15 shows the broad differences in theseload curves over the course of 24 hours.