Classification of Special Days in Short-Term Load Forecasting: The Spanish Case Study

López, María Dolores Ruiz; Sans, Carlos; Verdú, S.; Senabre, C.

doi:10.3390/en12071253

Cited by 29 publications

(24 citation statements)

References 43 publications

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“…In addition, it is found that some special days affect load trends rapidly in a short period based on the above analysis, which is also confirmed in the previous work [28], [32]. Accordingly, special days must be considered as an important factor in STLF, especially Chinese national legal holidays listed in Table 1.…”

Section: Features Selectionsupporting

confidence: 83%

A Multifactorial Short-Term Load Forecasting Model Combined With Periodic and Non-Periodic Features - A Case Study of Qingdao, China

et al. 2020

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In this paper, the load time-series measured from 2016 to 2018 in Qingdao is investigated in order to make a prediction more accurately by using an artificial neural network model combined with some regular and irregular features. The results of spectral analysis show that several periodic variations in diurnal, semidiurnal and weekly frequencies are prominent, and considered as critical parts of predictior variables of the training and test sets in this forecasting model. However, a significant decline of load happens during the national statutory festivals in China, and is more obvious with the longer holidays, which should be taken into account as abnormal conditions. Moreover, both temperatuer and hummity filtered out by mutual information method are also added as two essential weather factors to improve the prediction accuracy, especially in the hot summer. Finally, the comparative results of five different experiments in term of mean absolute percent error show that the forecasting model combined with all periodic and non-periodic factors outperforms the others with one single or a few of factors. This multifactorial model, taking fully account of internal charateristics of load data and external important influences, is proved more suitable to predict the load trend in Qingdao. INDEX TERMS Short-term load forcasting, artificial neural network, periodic and non-periodic factors, mean absolute percent error.

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Section: Features Selectionsupporting

confidence: 83%

A Multifactorial Short-Term Load Forecasting Model Combined With Periodic and Non-Periodic Features - A Case Study of Qingdao, China

et al. 2020

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“…To illustrate that this experiment shows better prediction results for the load forecasting of small power grids, this paper uses the data of a small power grid as the experimental data set, and uses the Seq2seq proposed in this paper to carry out load forecasting. In [38], the authors considered the impact of different types of day, which is important for load prediction. In the forecasting process, the weather (temperature, holidays, and humidity, etc.)…”

Section: Supplementary Experimentsmentioning

confidence: 99%

Research on Short-Term Load Prediction Based on Seq2seq Model

Gong

Mahato

et al. 2019

Energies

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Electricity load prediction is the primary basis on which power-related departments to make logical and effective generation plans and scientific scheduling plans for the most effective power utilization. The perpetual evolution of deep learning has recommended advanced and innovative concepts for short-term load prediction. Taking into consideration the time and nonlinear characteristics of power system load data and further considering the impact of historical and future information on the current state, this paper proposes a Seq2seq short-term load prediction model based on a long short-term memory network (LSTM). Firstly, the periodic fluctuation characteristics of users’ load data are analyzed, establishing a correlation of the load data so as to determine the model’s order in the time series. Secondly, the specifications of the Seq2seq model are given preference and a coalescence of the Residual mechanism (Residual) and the two Attention mechanisms (Attention) is developed. Then, comparing the predictive performance of the model under different types of Attention mechanism, this paper finally adopts the Seq2seq short-term load prediction model of Residual LSTM and the Bahdanau Attention mechanism. Eventually, the prediction model obtains better results when merging the actual power system load data of a certain place. In order to validate the developed model, the Seq2seq was compared with recurrent neural network (RNN), LSTM, and gated recurrent unit (GRU) algorithms. Last but not least, the performance indices were calculated. when training and testing the model with power system load data, it was noted that the root mean square error (RMSE) of Seq2seq was decreased by 6.61%, 16.95%, and 7.80% compared with RNN, LSTM, and GRU, respectively. In addition, a supplementary case study was carried out using data for a small power system considering different weather conditions and user behaviors in order to confirm the applicability and stability of the proposed model. The Seq2seq model for short-term load prediction can be reported to demonstrate superiority in all areas, exhibiting better prediction and stable performance.

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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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Classification of Special Days in Short-Term Load Forecasting: The Spanish Case Study

Cited by 29 publications

References 43 publications

A Multifactorial Short-Term Load Forecasting Model Combined With Periodic and Non-Periodic Features - A Case Study of Qingdao, China

A Multifactorial Short-Term Load Forecasting Model Combined With Periodic and Non-Periodic Features - A Case Study of Qingdao, China

Research on Short-Term Load Prediction Based on Seq2seq Model

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

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