This work presents a hybrid and hierarchical deep learning model for mid-term load forecasting. The model combines exponential smoothing (ETS), advanced Long Short-Term Memory (LSTM) and ensembling. ETS extracts dynamically the main components of each individual time series and enables the model to learn their representation. Multi-layer LSTM is equipped with dilated recurrent skip connections and a spatial shortcut path from lower layers to allow the model to better capture long-term seasonal relationships and ensure more efficient training. A common learning procedure for LSTM and ETS, with a penalized pinball loss, leads to simultaneous optimization of data representation and forecasting performance. In addition, ensembling at three levels ensures a powerful regularization. A simulation study performed on the monthly electricity demand time series for 35 European countries confirmed the high performance of the proposed model and its competitiveness with classical models such as ARIMA and ETS as well as state-of-the-art models based on machine learning.
Seasonal affective disorder (SAD) is considered to be a form of depression. As the sun sets earlier during daylight saving time, many people start to develop the signs of seasonal affective disorder. Jeff Janata, a professor of psychiatry and the director of psychology at University Hospitals Cleveland Medical Center reportedly said that the symptoms of SAD include irritation, excessive sleeping and loss of interest. Most people with seasonal affective disorder have symptoms that start in the fall and continue into the winter months. The lower levels of sunlight in the winter and fall, particularly in locations farther from the equator, are the main cause of seasonal affective disorder.
Heat demand prediction is a prominent research topic in the area of intelligent energy networks. It has been well recognized that periodicity is one of the important characteristics of heat demand. Seasonal-trend decomposition based on LOESS (STL) algorithm can analyze the periodicity of a heat demand series, and decompose the series into seasonal and trend components. Then, predicting the seasonal and trend components respectively, and combining their predictions together as the heat demand prediction is a possible way to predict heat demand. In this paper, STL-ENN-ARIMA (SEA), a combined model, was proposed based on the combination of the Elman neural network (ENN) and the autoregressive integrated moving average (ARIMA) model, which are commonly applied to heat demand prediction. ENN and ARIMA are used to predict seasonal and trend components, respectively. Experimental results demonstrate that the proposed SEA model has a promising performance.
NewAir, which makes small appliances like wine coolers and ice makers, takes on seasonal help for the summer and the winter holidays. The company's warehouse in Cypress, California, has about 20 employees and hires 10 during the periods when orders from online customers and retailers soar and there's more to pack and ship. During interviews, candidates learn that the company tracks employee productivity on a public scoreboard, and everyone knows the highest performers keep their jobs when the busy season ends.