Research on Electricity Load Forecasting and Demand Side Modeling Based on the Integration of Big Data and Machine Learning
DOI:
https://doi.org/10.13052/spee1048-5236.4415Keywords:
Electricity load forecasting, big data, machine learning, LSTM model, demand side modelingAbstract
In recent times, the application of machine learning algorithms in energy forecasting has expanded significantly, facilitated by the abundant data amassed through the integration of sensor technology and power distribution systems. This data richness presents opportunities for leveraging diverse machine learning techniques. Electric load forecasting and strategic planning are pivotal for driving national industrialization and urbanization, enabling the identification of demand-side load patterns and efficient resource allocation, thereby mitigating resource waste or power shortages and fostering economic growth. This study aims to delve into the prediction and analysis of demand-side loads, leveraging the synergy of big data and machine learning methodologies. Key findings reveal that the LSTM model outperforms SVR and DNN in terms of prediction error reduction and robustness. Specifically, for single load type prediction, LSTM achieves an RMSE of 18.65% and an MAE of 18.74%. When applied to multiple load types, LSTM further enhances its performance, with RMSE and MAE declining to 9.73% and 8.89%, respectively. Aggregate load forecasting emerges as an effective strategy to minimize prediction errors stemming from individual variability, with the three models achieving RMSEs of 5.01%, 5.94%, and 6.59%, respectively, underscoring improved predictive accuracy. Notably, apparent temperature significantly influences the load prediction process, exhibiting a strong negative correlation with the average daily load.
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