# Recurrent Neural Network Based Short-Term Load Forecast with Spline Bases and Real-Time Adaptation
###### Abstract  
Short-term load forecast (STLF) plays an important role in power system operations. This paper proposes a spline bases-assisted Recurrent Neural Network (RNN) for STLF with a semi-parametric model being adopted to determine the suitable spline bases for constructing the RNN model. To reduce the exposure to real-time uncertainties, interpolation is achieved by an adapted mean adjustment and exponentially weighted moving average (EWMA) scheme for finer time interval forecast adjustment. To circumvent the effects of forecasted apparent temperature bias, the forecasted temperatures issued by the weather bureau are adjusted using the average of the forecast errors over the preceding 28 days. The proposed RNN model is trained using 15-min interval load data from the Taiwan Power Company (TPC) and has been used by system operators since 2019. Forecast results show that the spline bases-assisted RNN-STLF method accurately predicts the short-term variations in power demand over the studied time period. The proposed real-time short-term load calibration scheme can help accommodate unexpected changes in load patterns and shows great potential for real-time applications. x 2021 11, 5930. [[https://doi.org/10.3390/app11135930](https://doi.org/10.3390/app11135930)]([https://doi.org/10.3390/app11135930](https://doi.org/10.3390/app11135930))  
Academic Editor [PERSON]  
Received: 4 June 2021  
Accepted: 22 June 2021  
Published: 25 June 2021  
**Published:**  
**Subfile:**  
(GRNN) [8] are among numerous ANN-based STLF techniques used in many control centers [9; 10]. A systematic review on the techniques of electricity demand forecasting methods is available [11]. This overview includes regression, time series analysis, ANN, support vector machines and certain bottom-up approaches. Load demand time series are nonlinear and non-stationary, with time-dependent dynamic behaviors. Thus, in our view, the Recurrent Neural Network (RNN) is a more suitable model for this purpose, as it can consider the temporal context in the feedback connections and as it has a nonlinear property [12]. Several RNN models for STLF have been proposed. In [13], system net load profiles are modeled by RNNs as the outputs of dynamic systems influenced by weather, time and other environmental factors. A pooling-based deep RNN for household load forecasts is proposed in [14], where the focus is to provide more suitable training samples to the network. Besides the cyclic patterns, the load demand is also affected by several exogenous variables, including the prevailing weather conditions, the calendar effect, and the general randomness inherent in individual's behaviors. With climate change and the increase of behind-the-meter renewable integrations, it is challenging to effectively integrate the various exogenous variables into the STLF model and provide accurate load forecasts. In [15], an ensemble of radial basis function neural networks (RBFNNs) is proposed where exogenous features and features extracted from load series, through long short-term memory (LSTM) networks and multi-resolution wavelet transforms in various timescales, are used to train the RBFNNs. While in [16], a hybrid algorithm that combines similar day selection, empirical mode decomposition, and LSTM neural networks to construct a prediction model for STLF. This study develops a RNN-based short-term (i.e., over the next few hours, the next day, or the next week) load forecast model for the Taiwan Power Company (TPC) system. It is implemented through a semi-parametric regression framework to catch general daily demand behaviors. The STLF method uses the least absolute shrinkage and selection operator (Lasso) regression [17] and an adaptive Lasso [18] to select suitable variables. It includes multi-resolution basis functions [19] and cubic B-spline basis functions in the RNN model, in order to catch useful patterns, reduce the overfitting problem, and increase the forecast accuracy.