Energy Demand Forecasting and Economic Regulation System Optimization Based on Neural Network

This paper proposes an energy demand forecasting and economic regulation system optimization model based on neural network. The model uses the MLP neural network structure and is trained through multidimensional data such as historical energy consumption, climate change and socio-economic indicators to provide high-precision energy demand forecasts. Compared with the conventional statistics, the neural network can capture the non-linear relation in the data, and the precision of the forecast is greatly increased. Experimental results show that the prediction model based on neural networks improves the accuracy of energy demand prediction by about 15 % and can provide more flexible adjustment strategies. In terms of economic regulation, this paper designs an optimization regulation mechanism based on the prediction results, dynamically adjusts energy production scheduling through genetic algorithms and particle swarm optimization algorithms, optimizes energy production and consumption structure, and maximizes energy utilization efficiency. Through simulation experiments, the system not only effectively reduces the cost of energy production, but also achieves significant results in improving energy utilization and reducing waste. Specific experimental results show that the economic regulation strategy can save energy costs by up to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1 2 \%}$</tex> and effectively reduce economic fluctuations caused by supply shortages.