This work presents a Long Short-Term Memory (LSTM) network for forecasting a monthly electricity demand time series with a one-year horizon. The novelty of this work is the use of pattern representation of the seasonal time series as an alternative to decomposition. Pattern representation simplifies the complex nonlinear and nonstationary time series, filtering out the trend and equalizing variance. Two types of patterns are defined: x-pattern and y-pattern. The former requires additional forecasting for the coding variables. The latter determines the coding variables from the process history. A hybrid approach based on x-patterns turned out to be more accurate than the standard LSTM approach based on a raw time series. In this combined approach an x-pattern is forecasted using a sequence-to-sequence LSTM network and the coding variables are forecasted using exponential smoothing. 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 to classical models such as ARIMA and exponential smoothing as well as the MLP neural network model.
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.
Pattern similarity-based methods are widely used in classification and regression problems. Repeated, similar-shaped cycles observed in seasonal time series encourage to apply these methods for forecasting. In this paper we use the pattern similarity-based methods for forecasting monthly electricity demand expressing annual seasonality. An integral part of the models is the time series representation using patterns of time series sequences. Pattern representation ensures the input and output data unification through trend filtering and variance equalization. Consequently, pattern representation simplifies the forecasting problem and allows us to use models based on pattern similarity. We consider four such models: nearest neighbor model, fuzzy neighborhood model, kernel regression model and general regression neural network. A regression function is constructed by aggregation output patterns with weights dependent on the similarity between input patterns. The advantages of the proposed models are: clear principle of operation, small number of parameters to adjust, fast optimization procedure, good generalization ability, working on the newest data without retraining, robustness to missing input variables, and generating a vector as an output. In the experimental part of the work the proposed models were used to forecasting the monthly demand for 35 European countries. The model performances were compared with the performances of the classical models such as ARIMA and exponential smoothing as well as state-of-the-art models such as multilayer perceptron, neuro-fuzzy system and long short-term memory model. The results show high performance of the proposed models which outperform the comparative models in accuracy, simplicity and ease of optimization.
So, I compared the model with ARIMA and a few interesting findings. Firstly, there doesn't appear to be any seasonal component in the data - when decomposed with statsmodels, the series simply shows a straight line. Also, ARIMA showed a mean percentage error of 23%, whereas for LSTM it was just over 8%. The daily fluctuations in electricity consumption is quite volatile, so it looks like LSTM has an advantage over ARIMA here in that it is accounting for the inherent volatility in the series. While ARIMA would usually need to be combined with a model such as GARCH to estimate this volatility, the inherent nature of LSTM allows it to handle sequential data and in this case it looks like it's handling the volatility quite well.
Long Short-Term Memory (LSTM) neural network is an enhanced Recurrent Neural Network (RNN) that has gained significant attention in recent years. It solved the vanishing and exploding gradient problems that a standard RNN has and was successfully applied to a variety of time-series forecasting problems. In power systems, distribution feeder long-term load forecast is a critical task many electric utility companies perform on an annual basis. The goal of this task is to forecast the load change on existing distribution feeders for the next few years. The forecasted results will be used as input in long-term system planning studies to determine necessary system upgrades so that the distribution system can continue to operate reliably during normal operation and contingences. This research proposed a comprehensive hybrid model based on LSTM neural network for this classic and important forecasting task. It is not only able to combine the advantages of top-down and bottom-up forecasting models but also able to leverage the time-series characteristics of multi-year data. This paper firstly explains the concept of LSTM neural network and then discusses the steps of feature selection, feature engineering and model establishment in detail. In the end, a real-world application example for a large urban grid in West Canada is provided. The results are compared to other models such as bottom-up, ARIMA and ANN. The proposed model demonstrates superior performance and great practicality for forecasting long-term peak demand for distribution feeders.