Browsing by Author "Alkhattabi, Moayad"

Now showing 1 - 1 of 1

Restricted
RESEARCH ON ELECTRICITY CONSUMPTION PREDICTION BASED ON DEEP INTEGRATION MODELS
(Harbin Institute of Technology, 2024) Alkhattabi, Moayad; Chen, Ying
With the development of power system and the improvement of intelligence level, power consumption forecast plays a vital role in power system operation management, energy dispatching and market trading. Effective power demand forecasting is the key to power system planning and operation, and is of great significance to achieve safe, efficient and sustainable energy supply. However, the traditional deep learning model has the problem of falling into local optimal in the optimization process, which leads to the unstable performance of the model. To overcome this problem, the particle swarm optimization (PSO) algorithm is improved in this study to improve the performance of deep learning models in power consumption prediction tasks. This study collates and summarizes the challenges encountered when dealing with nonlinear, non-stationary, and high-dimensional data. To overcome these challenges, an improved particle swarm optimization (PSO) algorithm is proposed to optimize the parameters of deep learning models, thereby enhancing the model's fitting ability and generalization performance. The improved PSO algorithm in this study adopts dynamic weight adjustment and multi-stage optimization strategy, which effectively realizes the balance between global search and local search, and greatly improves the performance of the model in complex power systems. In the process of model construction, the stack ensemble learning method is adopted, and five machine learning methods including long short-term memory network (LSTM) are used to build a deeply integrated power consumption model prediction model. To verify the validity and applicability of the model, extensive experimental tests are carried out on real world power system data sets. The experimental results show that the PSO-Stacking model in this study has a root-mean-square error (RMSE) of 0.095, a mean absolute error (MAE) of 0.074, and an R square (R²) of 0.862, which are robust performance indicators. These results demonstrate the effectiveness of improved particle swarm optimization algorithm and stacked ensemble learning model in power consumption prediction tasks. Compared with the traditional deep learning model, the optimized deep learning model using the improved PSO algorithm shows considerable improvement in accuracy, stability and response speed.
28 0