# Short-Term Load Forecasting Based on Spiking Neural P Systems
###### Abstract  
Short-term load forecasting is a significant component of safe and stable operations and economical and reliable dispatching of power grids. Precise load forecasting can help to formulate reasonable and effective coordination plans and implementation strategies. Inspired by the spiking mechanism of neurons, a nonlinear spiking neural P (NSNP) system, a parallel computing model, was proposed. On the basis of SNP systems, this study exploits a fresh short-term load forecasting model, termed as the LF-NSNP model. The LF-NSNP model is essentially a recurrent-like model, which can effectively capture the correlation between the temporal features of the electric load sequence. In an effort to validate the effectiveness and superiority of the proposed LF-NSNP model in short-term load forecasting tasks, tests were conducted on datasets of different time and different variable types, and the predictive competence of various baseline models was compared. short-term load forecasting; the electric load sequence; recurrent-like model; spiking neural P systems +
Footnote †: journal: 1008  
long short-term memory (LSTM), gated recurrent unit (GRU), and so on. In recent years, a variety of deep neural network models are already used to solve power load forecasting problems. For instance, [PERSON] et al. [15] developed a load forecasting method derived from deep neural networks. [PERSON] et al. [16] discussed a deep neural network regression method for short-term load forecasting. [PERSON] et al. [17] proposed a conditional residual model for probabilistic load forecasting. [PERSON] et al. [18] discussed a deep reinforcement learning method for short-term load forecasting. Although these models have shown good prediction performance, however, a power system is a complex dynamic system, so load forecasting is still a challenging task. This study focuses on developing a new recurrent-like prediction model for load forecasting tasks. Spiking neural P (SNP) systems [19] are a novel batch of distributed parallel computing models, enlightened from the framework of a biological nervous system. Inspired by spiking neurons, SNP systems fall into the third generation of neural networks. Nonlinear spiking neural P (NSNP) systems [20] are the nonlinear variants of SNP systems. The nonlinear spike mechanism is an important feature of an NSNP system, which is different from other systems. In an NSNP system, there is a nonlinear firing rule and a unit for storing states in every neuron, and the state is evolved by the nonlinear spiking rule. Therefore, NSNP systems are particularly suitable for characterizing a dynamical system. This study focuses on developing a brand-new model for a short-term load forecasting problem. For this reason, NSNP systems are introduced to exploit a novel short-term load forecasting model, termed as the LF-NSNP model. Due to its structure, the LF-NSNP model is a recurrent-like model, that is, a variant of the SNP system combined with recurrent neural networks (RNN). The LF-NSNP model has the ability to effectively capture the correlation between the temporal features of the electric load sequence. A benchmark load dataset is used to test and verify the exploited LF-NSNP model and 11 different baseline prediction models. The contributions of this study can be summarized as follows:  
1. We propose a variant of NSNP systems, which is inspired from the nonlinear spiking mechanism of biological neurons. 2. Based on the variant, we deduce a new type of neuron model, NSNP neuron model, which is a recurrent-like neuron model. 3. Based on the NSNP neuron model, we develop a prediction model for short-term load forecasting, called the LF-NSNP model. The LF-NSNP model can be implemented in the RNN framework due to its recurrent-like structure. 4. Extensive experiment is conducted to verify the effectiveness of the proposed LF-NSNP model for short-term load forecasting. The remainder of this paper is organized as follows. Section 2 briefly reviews NSNP systems and then discusses in detail the proposed LF-NSNP model.