Abstract: Forecasting electricity load is important for various purposes like planning, operation and control. Forecasts can save operating and maintenance costs, increase the reliability of power supply and delivery systems, and correct decisions for future development. This paper compares various time series methods to forecast 24 hours ahead of electricity load. The methods considered are the Holt-Winters smoothing, SARIMA Modeling, LSTM Network, Fbprophet and Tensorflow probability. The performance of each method is evaluated by using the forecasting accuracy criteria namely, the Mean Absolute Error and Root Mean Square Error. The National Renewable Energy Laboratory (NREL) residential energy consumption data are used to train the models. The results of this study show that SARIMA model is superior to the others for 24 hours ahead forecasts. Furthermore, a Bagging technique is used to make the predictions more robust. The obtained results show that by Bagging multiple time-series forecasts we can improve the robustness of the models for 24 hour ahead electricity load forecasting.
Abstract: The aim of this paper is to select the most accurate
forecasting method for predicting the future values of the
unemployment rate in selected European countries. In order to do so,
several forecasting techniques adequate for forecasting time series
with trend component, were selected, namely: double exponential
smoothing (also known as Holt`s method) and Holt-Winters` method
which accounts for trend and seasonality. The results of the empirical
analysis showed that the optimal model for forecasting
unemployment rate in Greece was Holt-Winters` additive method. In
the case of Spain, according to MAPE, the optimal model was double
exponential smoothing model. Furthermore, for Croatia and Italy the
best forecasting model for unemployment rate was Holt-Winters`
multiplicative model, whereas in the case of Portugal the best model
to forecast unemployment rate was Double exponential smoothing
model. Our findings are in line with European Commission
unemployment rate estimates.
Abstract: Due to the liberalization of countless electricity markets, load forecasting has become crucial to all public utilities for which electricity is a strategic variable. With the goal of contributing to the forecasting process inside public utilities, this paper addresses the issue of applying the Holt-Winters exponential smoothing technique and the time series analysis for forecasting the hourly electricity load curve of the Italian railways. The results of the analysis confirm the accuracy of the two models and therefore the relevance of forecasting inside public utilities.