Background Energy consumption forecasting is crucial to enhance energy distribution, planning, and smart grid control. Energy and especially electricity demands are perpetually varying because of urbanization, climate change, and due to digitalization, which makes the prediction of the consumption patterns of utility consumption even more difficult, as utility providers struggle to maintain the maximum precision in their forecasts. Problem Statement Current conventional approaches to forecasting, like ARIMA, often fail to give accurate results, either because they do not perfectly capture spatial aspects of energy data, or do not model long-term temporal aspects of energy data. In addition, newer methods in deep learning, like LSTM and GRU, may also prove unable to capture long-term temporal trends as well. In addition to that, poor model tuning and minimal feature engineering make the models less reliable and less generalizable. They did not have any attention mechanisms, in addition to weak hyperparameter tuning, which led to increased errors in making predictions and higher training times. Methodology As a way to overcome such gaps, in this research, a recently conceived hybrid deep learning framework has been deployed, which is a unification of Convolutional Neural Network (CNN) to accomplish feature extraction, Bidirectional LSTM (BiLSTM) to learn a sequence, and Genetic Algorithm (GA) to optimize hyperparameters, which is referred to as CBG-EnergyNet. The model takes advantage of real-world energy data collection and uses complete data preprocessing, sliding windows calculation of the function that uses the features, and large optimization topics such as Adam and Bayesian tuning as well. Results A number of performance indicators were used to test the proposed model and compared to such models as ARIMA, LSTM, GRU, and CNN-LSTM. CBG-EnergyNet: MAE: 7.83, RMSE: 10.12, MAPE: 7.05%, R 2 Score: 0,93, Training Time: 11.5 s. The outcomes are sufficient evidence that this model helps demonstrate better performance than the measures of the existing methods in all key metrics. Conclusions The CBG-EnergyNet model presents a very important breakthrough in energy consumption prediction since the combination of spatial and temporal learning proposes a very considerable improvement in estimation performance of energy consumption predictions by optimizing hyperparameters. It provides high accuracy and is hence a very important practical tool for energy planning in the real world, especially in a smart grid network and the system of demand-side management as well. The potential of the model is an opportunity to be applied in large-scale energy in the future due to its generalizability as well as the limited computational demands.

Analysis of time series modeling for energy consumption prediction using the CBG-EnergyNet framework

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2026年3月2日

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Jiang Guan Min

摘要

Background

Energy consumption forecasting is crucial to enhance energy distribution, planning, and smart grid control. Energy and especially electricity demands are perpetually varying because of urbanization, climate change, and due to digitalization, which makes the prediction of the consumption patterns of utility consumption even more difficult, as utility providers struggle to maintain the maximum precision in their forecasts.

Problem Statement

Current conventional approaches to forecasting, like ARIMA, often fail to give accurate results, either because they do not perfectly capture spatial aspects of energy data, or do not model long-term temporal aspects of energy data. In addition, newer methods in deep learning, like LSTM and GRU, may also prove unable to capture long-term temporal trends as well. In addition to that, poor model tuning and minimal feature engineering make the models less reliable and less generalizable. They did not have any attention mechanisms, in addition to weak hyperparameter tuning, which led to increased errors in making predictions and higher training times.

Methodology

As a way to overcome such gaps, in this research, a recently conceived hybrid deep learning framework has been deployed, which is a unification of Convolutional Neural Network (CNN) to accomplish feature extraction, Bidirectional LSTM (BiLSTM) to learn a sequence, and Genetic Algorithm (GA) to optimize hyperparameters, which is referred to as CBG-EnergyNet. The model takes advantage of real-world energy data collection and uses complete data preprocessing, sliding windows calculation of the function that uses the features, and large optimization topics such as Adam and Bayesian tuning as well.

Results

A number of performance indicators were used to test the proposed model and compared to such models as ARIMA, LSTM, GRU, and CNN-LSTM. CBG-EnergyNet: MAE: 7.83, RMSE: 10.12, MAPE: 7.05%, R 2 Score: 0,93, Training Time: 11.5 s. The outcomes are sufficient evidence that this model helps demonstrate better performance than the measures of the existing methods in all key metrics.

Conclusions

The CBG-EnergyNet model presents a very important breakthrough in energy consumption prediction since the combination of spatial and temporal learning proposes a very considerable improvement in estimation performance of energy consumption predictions by optimizing hyperparameters. It provides high accuracy and is hence a very important practical tool for energy planning in the real world, especially in a smart grid network and the system of demand-side management as well. The potential of the model is an opportunity to be applied in large-scale energy in the future due to its generalizability as well as the limited computational demands.