## 4 Case Study
### Sensitivity Analysis of Load Influencing Factors at Seasonal Scales  
In order to verify the sensitivity of the load forecast results to each environmental factor, which is characterized by the error in the load forecast results after the change in characteristics by sequentially varying the individual climate factor data to 10% of the true data. Meanwhile, in order to combine the local geographic features and living habits of the residents more closely, the data are analyzed according to the seasonal scale, in which February, March, April and May are in spring, June, July and August are in summer, and September and October are in autumn. The distribution of load forecasting errors due to changes in climate characteristics at the seasonal scale is shown in Figure 4. The screened seven key influences are synthesized as temperature, humidity, wind speed and rainfall to simplify the analysis. The different seasons in Figure 4 correspond to bars with different patterns, and specific values are labeled above the bars corresponding to the different factors for each season to make the results clearer. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Category** & **Method** & **RMSE** & **MAE** & **MAPE** \\ \hline \multirow{6}{*}{Regression load forecasting} & & BP [34] & 192.5175 & 160.7176 & 0.0465 \\  & Unfiltered & CNN [35] & 193.9727 & 157.0710 & 0.0460 \\  & Key Factors & LSTM [36] & 190.8181 & 155.0108 & 0.0443 \\  & & RF [37] & 188.6064 & 160.7783 & 0.0472 \\  & & QGCT-BiLSTM & 205.1504 & 162.4930 & 0.0469 \\ \cline{2-6}  & & BP [34] & 170.2113 & 141.0954 & 0.0413 \\  & Filtered Key & CNN [35] & 181.3503 & 155.6983 & 00455 \\  & Factors & LSTM [36] & 172.0750 & 137.7569 & 0.0399 \\  & & RF [37] & 185.5207 & 160.2694 & 0.0471 \\  & & QGCT-BiLSTM & 145.2686 & 113.6537 & 0.0255 \\ \hline \multirow{3}{*}{Time-series load forecasting} & & BP [34] & 321.8326 & 276.0081 & 0.0779 \\  & CNN [35] & 429.9377 & 356.0354 & 0.1027 \\ \cline{1-1}  & LSTM [36] & 364.3561 & 297.9974 & 0.0834 \\ \cline{1-1}  & RF [37] & 300.4929 & 261.7523 & 0.0740 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation of errors in load forecasting results of different methods. With a subtropical monsoon climate and four distinct seasons, the Fujian region has more extreme weather such as typhoons in summer and windy spring and autumn, and is located on the southeast coast with a pronounced oceanic climate. The unique geographic location and climate characteristics have a significant impact on load changes. The sensitivities of the load forecast to changes in the influencing factors during spring are humidity, wind speed, temperature and rainfall in descending order. This is due to the fact that Fujian province of China is in the early part of the rainy season at this time with high humidity and increased demand for dehumidification equipment from residents, resulting in load sensitivity to changes in humidity. Higher wind speeds in spring lead to significant load changes in wind power equipment especially in coastal areas. Also, the moderate temperatures and rainfall in the spring had a limited impact on residential and industrial electricity consumption. The sensitivities of load forecasts to changes in influencing factors during summer are temperature, rainfall, wind speed and humidity in descending order. The high temperatures in Fujian province of China during summer lead to a surge in demand for cooling by residents, resulting in loads that are highly sensitive to changes in temperature. Moreover, summer is the season of heavy rainfall and typhoons, which have a greater impact on the demand for electricity for residents and industrial activities. Residents are less concerned about humidity in the summer resulting in lower load sensitivity to humidity. The sensitivities of load forecasts to changes in influencing factors during autumn are temperature, wind speed, rainfall and humidity in descending order.