Research on Impact Factors, Forecasting, and Optimization of Electricity and Photovoltaic Power Generation Based on Integrated Analysis Methods

With the global growth in energy demand and environmental challenges, studying the relationship between electricity supply and its influencing factors is crucial for China's energy policy and clean energy development. This paper analyzes data on electricity production, revealing seasonal fluctuations through visualization. A time-series prediction model indicates a continuous increase in electricity supply from 2024 to 2060. It identifies factors influencing photovoltaic power plant construction using bi-directional indexing and employs a combination weight method to determine indicator weights. The TOPSIS method scores domestic provinces to identify the best locations for photovoltaic plants in Xinjiang, aiming for a maximum PV generation capacity of approximately <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1.37 \times \mathbf{1 0}^{14}$</tex> kWh. Additionally, recent data on monthly power generation from nuclear, wind, photovoltaic, hydropower, and solar sources is analyzed. The SSA-MLP algorithm constructs a time-series prediction model, indicating that clean energy generation will reach <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{6 2 \%}$</tex> of total generation by 2060, falling short of carbon neutrality targets.