Multi-horizon short-term load forecasting using hybrid of LSTM and modified split convolution

Abstract

Precise short-term load forecasting (STLF) plays a crucial role in the smooth operation of power systems, future capacity planning, unit commitment, and demand response. However, due to its non-stationary and its dependency on multiple cyclic and non-cyclic calendric features and non-linear highly correlated metrological features, an accurate load forecasting with already existing techniques is challenging. To overcome this challenge, a novel hybrid technique based on long short-term memory (LSTM) and a modified split-convolution (SC) neural network (LSTM-SC) is proposed for single-step and multi-step STLF. The concatenating order of LSTM and SC in the proposed hybrid network provides an excellent capability of extraction of sequence-dependent features and other hierarchical spatial features. The model is evaluated by the Pakistan National Grid load dataset recorded by the National Transmission and Dispatch Company (NTDC). The load data is pre-processed and multiple other correlated features are incorporated into the data for performance enhancement. For generalization capability, the performance of LSTM-SC is evaluated on publicly available datasets of American Electric Power (AEP) and Independent System Operator New England (ISO-NE). The effect of temperature, a highly correlated input feature, on load forecasting is investigated either by removing the temperature or adding a Gaussian random noise into it. The performance evaluation in terms of RMSE, MAE, and MAPE of the proposed model on the NTDC dataset are 500.98, 372.62, and 3.72% for multi-step while 322.90, 244.22, and 2.38% for single-step load forecasting. The result shows that the proposed method has less forecasting error, strong generalization capability, and satisfactory performance on multi-horizon.

Advanced series decomposition with a gated recurrent unit and graph convolutional neural network for non-stationary data patterns

“…Despite significant research efforts, achieving high accuracy in STLF remains a complex endeavor due to the non-stationarity of electrical load data and the prediction of long-term dependencies [55]. Models such as Long Short-Term Memory (LSTM) networks and their bidirectional variants (BiLSTM) are used to forecast demand-side load across different time horizons (Ullah I, et al [56]). Gated Recurrent Unit (GRU) models have found applications in forecasting short-term load for electric vehicle (EV) charging stations and battery state-of-charge predictions [57,58].…”

Real-Time Load Forecasting and Adaptive Control in Smart Grids Using a Hybrid Neuro-Fuzzy Approach

“…The author in [25] employed a solitary neural network to acquire real-time knowledge of the tracking control aspect of this control assignment. Additionally, we incorporate a control term that enhances the resilience of the suggested control solution by addressing approximator error and external disturbances.…”