Application of Discrete-Interval Moving Seasonalities to Spanish Electricity Demand Forecasting during Easter

Trull, Óscar; García-Díaz, J. Carlos; Troncoso, Alicia

doi:10.3390/en12061083

Cited by 21 publications

(20 citation statements)

References 39 publications

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“…Taylor by including the double [65] and triple seasonal [66] Holt-Winters (HWT), and an adjustment using the first autocorrelation error (AR1), that improves the forecasts [67]. Trull et al use discrete-interval moving seasonalities to model Easter holidays [68].…”

Section: Basic Holt-winters Methodsmentioning

confidence: 99%

Electricity Forecasting Improvement in a Destination Using Tourism Indicators

2019

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The forecast of electricity consumption plays a fundamental role in the environmental impact of a tourist destination. Poor forecasting, under certain circumstances, can lead to huge economic losses and air pollution, as prediction errors usually have a large impact on the utilisation of fossil fuel-generation plants. Due to the seasonality of tourism, consumption in areas where the industry represents a big part of the economic activity follows a different pattern than in areas with a more regular economic distribution. The high economic impact and seasonality of the tourist activity suggests the use of variables specific to it to improve the electricity demand forecast. This article presents a Holt–Winters model with a tourism indicator to improve the effectiveness on the electricity demand forecast in the Balearic Islands (Spain). Results indicate that the presented model improves the accuracy of the prediction by 0.3%. We recommend the use of this type of model and indicator in tourist destinations where tourism accounts for a substantial amount of the Gross Domestic Product (GDP), we can control a significant amount of the flow of tourists and the electrical balance is controlled mainly by fossil fuel power plants.

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Section: Basic Holt-winters Methodsmentioning

confidence: 99%

Electricity Forecasting Improvement in a Destination Using Tourism Indicators

2019

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“…This scheme to initialize the level was also adopted by Hyndman et al [18]. Hyndman introduced new initialization methods incorporated in the exponential smoothing methodology based on state spaces and included them in the "forecast" package of the statistical software R. Trull, García-Díaz, and Troncoso [19] also used a series decomposition using STL methods of various seasonality, in order to obtain initial values in discrete seasonality. Some papers [20][21][22] analyze the initial values but very succinctly.…”

Section: Related Workmentioning

confidence: 99%

Initialization Methods for Multiple Seasonal Holt–Winters Forecasting Models

2020

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The Holt-Winters models are one of the most popular forecasting algorithms. As well-known, these models are recursive and thus, an initialization value is needed to feed the model, being that a proper initialization of the Holt-Winters models is crucial for obtaining a good accuracy of the predictions. Moreover, the introduction of multiple seasonal Holt-Winters models requires a new development of methods for seed initialization and obtaining initial values. This work proposes new initialization methods based on the adaptation of the traditional methods developed for a single seasonality in order to include multiple seasonalities. Thus, new methods to initialize the level, trend, and seasonality in multiple seasonal Holt-Winters models are presented. These new methods are tested with an application for electricity demand in Spain and analyzed for their impact on the accuracy of forecasts. As a consequence of the analysis carried out, which initialization method to use for the level, trend, and seasonality in multiple seasonal Holt-Winters models with an additive and multiplicative trend is provided.

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“…If only two seasonal patterns are considered, seasons modify the trend, whereas if three seasonalities are considered, the intra-year seasonal parameter should be influenced. The calendar also has a huge influence on the predictions and parameters, that must deal with some irregularities of the series [16][17][18]. Therefore, forecasters should always pay close attention to the parameters values.…”

Section: Introductionmentioning

confidence: 99%

Stability of Multiple Seasonal Holt-Winters Models Applied to Hourly Electricity Demand in Spain

2020

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Electricity management and production depend heavily on demand forecasts made. Any mismatch between the energy demanded with respect to that produced supposes enormous losses for the consumer. Transmission System Operators use time series-based tools to forecast accurately the future demand and set the production program. One of the most effective and highly used methods are Holt-Winters. Recently, the incorporation of the multiple seasonal Holt-Winters methods has improved the accuracy of the predictions. These forecasts, depend greatly on the parameters with which the model is constructed. The forecasters need to deal with these parameters values when operating the model. In this article, the parameters space of the multiple seasonal Holt-Winters models applied to electricity demand in Spain is analysed and discussed. The parameters stability analysis leads to forecasters better understanding the behaviour of the predictions and managing their exploitation efficiently. The analysis addresses different time windows, depending on the period of the year as well as different training set sizes. The results show the influence of the calendar effect on these parameters and if it is necessary or not to update them in order to obtain a good accuracy over time.

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Application of Discrete-Interval Moving Seasonalities to Spanish Electricity Demand Forecasting during Easter

Cited by 21 publications

References 39 publications

Electricity Forecasting Improvement in a Destination Using Tourism Indicators

Electricity Forecasting Improvement in a Destination Using Tourism Indicators

Initialization Methods for Multiple Seasonal Holt–Winters Forecasting Models

Stability of Multiple Seasonal Holt-Winters Models Applied to Hourly Electricity Demand in Spain

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