# Power Load Demand Forecasting Model and Method Based on Multi-Energy Coupling
## 1 Introduction
The construction of load forecasting model is different according to the forecast scenario and forecast content. For example, [PERSON] et al. constructed the EMD-mRMR-FOA-GRNN model (a hybrid model which combines empirical mode decomposition (EMD)), minimal redundancy maximal relevance (mRMR), general regression neural network (GRNN) with fruit fly optimization algorithm (FOA)) aiming at the nonlinearity and randomness of power load series, which is a short-term load forecasting method, and its forecasting error is [\(-\)1%, \(+\)1%] [16]; [PERSON] et al. constructed a model in which the extreme learning machine (ELM), support vector machine (SVM), and least squares support vector machine (LSSVM) were used to forecast the short-term wind speed for short-term wind power forecasting, aiming at the stochastic and intermittent nature of wind power [17]. It is not difficult to find that the artificial intelligence algorithm can achieve relatively accurate load forecasting, and researchers usually improve the forecasting accuracy through trend decomposition or algorithms combination, which does not explain the impact of its key impact factors on the load sequence from the perspective of internal relations. The summary of partial load forecasting methods is shown in Table 1. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Reference** & **Forecast Scenario** & **Forecast Model** & **Scope of Application** \\ \hline
1 & [16] & Electricity market transaction & EMD-mRMR- FOA-GRNN & Short-term load forecasting (STLF) \\ \hline
2 & [17] & Wind power generation & ELM, SVM, and LSSVM & Short-term wind power forecast \\ \hline
3 & [18] & Electricity market transaction & Gaussian process regression & STLF \\ \hline
4 & [19] & Regional power planning & Box-cox transformation quantile regression and load relation factor identification & Medium and long term load forecasting and load probability density forecasting \\ \hline
5 & [20] & — & SVM forecasting based on cointegration—granger causality test and seasonal decomposition & Monthly load forecast \\ \hline
6 & [21] & Greenhouse gas emissions & Adaptive grey model & Annual greenhouse gas emissions \\ \hline
7 & [22] & — & A combined method that is based on the fuzzy time series (FTS) and convolutional neural networks (CNN) & STLF \\ \hline
8 & [23] & Generation planning and scheduling & EMD-mRMR-PSO (particle swarm optimization)-LSSVM & STLF \\ \hline
9 & [24] & China’s energy efficiency & Three-dimensional decomposition model and small-sample hybrid model & Annual energy efficiency forecast \\ \hline
10 & [25] & Energy consumption & EEMD-ISFLA (Shuffled Frog and Leaping Algorithm)-LSSVM & Annual energy consumption forecast \\ \hline
11 & [26] & Electric power dispatching & CNN-LSTM (long short term memory) & STLF \\ \hline
12 & [27] & Algorithm & SVM & Classification and forecasting problems \\ \hline
13 & [28] & Power planning, operation and maintenance & MFO (Moth-Flame Optimization algorithm) & Annual load forecast \\ \hline
14 & [29] & Electricity market trading and electricity distribution plan & Empirical mode decomposition, seasonal adjustment, PSO, and LSSVM model & STLF \\ \hline
15 & [30] & Power assisted decision making & Weighted LSSVM based approach for time series forecasting & Annual load forecast \\ \hline
16 & [31] & Demand for electricity & Deep learning framework & Seasonal and diurnal power demand forecasting \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Introduction of partial load forecasting methods. Due to its short development history, the medium- and long-term power load demand forecasting under the multi-energy coupling scenario is characterized by a small number of samples, many influencing factors and complex interrelationships. The feature vector selection method can preliminary weaken the problems that there are many influencing factors and their interrelationships are complex [16; 23].