Deep Learning Lights the Way: Forecasting Electricity Consumption

By Muhammad OsamaReviewed by Susha Cheriyedath, M.Sc.Mar 25 2024

In an article published in the journal Scientific Reports, researchers explored the application of deep learning algorithms for predicting electricity consumption. They introduced an accurate forecasting model that can assist in efficient energy management and planning. Long short-term memory (LSTM) algorithms were utilized to forecast future electricity consumption.

Background

Building energy management systems (BEMS) comprise both hardware and software technologies and play a pivotal role in monitoring and controlling energy usage within buildings. These integrated computerized systems track various energy-consuming equipment, including lighting, power systems, and heating, ventilation, and air conditioning (HVAC) systems.

Beyond mere energy conservation, BEMS enhances the quality of energy supply and provides valuable insights into usage patterns. By integrating data from sensors, meters, and other sources, BEMS enables building owners and facility managers to make data-driven decisions. Real-time information on energy consumption empowers them to enhance energy efficiency.

About the Research

In the present paper, the authors introduced an LSTM-based prediction technique to effectively forecast future electricity consumption in buildings. LSTM, a type of recurrent neural network (RNN) specifically designed to capture long-term dependencies in sequential data, proved suitable for analyzing time series patterns, such as electricity consumption data.

The researchers enhanced predictive accuracy by integrating LSTM networks into BEMS. The study gathered real-world electricity demand data from the OpenEI data portal, specifically hourly consumption records from a hospital building over a year. These records were sourced from various monitoring devices within the hospital, including sensors and smart meters. By utilizing this dataset, the researchers aimed to predict future electricity demand accurately, emphasizing the importance of efficient energy management in buildings.

The collected time series data were transformed into input and output matrices, organized into a format suitable for use with the LSTM algorithm. Typically, this format consisted of a 3-dimensional (3D) tensor with dimensions [samples, time-steps, input-features].

Each data sample represented a specific time step and contained a single feature, indicating the electricity consumption at that time step. Following data preprocessing, the LSTM algorithm was trained using a portion of the dataset. This training process entailed adjusting the weights and biases of the LSTM network based on the input data and corresponding output labels, aiming to minimize the disparity between predicted and actual consumption.

Throughout the training phase, the LSTM network learned to discern patterns and trends in the electricity consumption data. Leveraging its memory cells, it retained critical information from previous time steps and employed this knowledge to forecast future consumption patterns. Once trained, the LSTM network became capable of predicting future electricity consumption, generating forecasts for future time steps by inputting historical electricity consumption data.

Moreover, the proposed model was optimized by utilizing optimizers and hyperparameters. The researchers experimented with different optimizers, including stochastic gradient descent (SGD), adaptive moment estimation (Adam), and root mean square propagation (RMSprop), to find the most suitable one for updating weights and learning rate.

Additionally, the study fine-tuned hyperparameters such as the number of neurons in the network, the activation function, and the number of epochs for training, aiming to create an effective neural network structure that improves predictive model accuracy. Furthermore, the researchers conducted experiments with different combinations of optimizers and hyperparameters to find the optimal configuration. Model performance was evaluated using metrics such as R-squared value, which measures how well the regression line fits the data while minimizing the mean absolute loss.

Research Findings

The outcomes showed that the new approach achieved a remarkable 95% accuracy rate in forecasting electricity consumption in advance. This indicated the effectiveness of the proposed methodologies and the utilization of LSTM networks in accurately predicting electricity consumption. The high accuracy rate is crucial for energy management practices as it enables building operators to make informed decisions regarding energy usage and optimize energy efficiency.

The study also highlighted the importance of advanced deep learning techniques in improving energy management practices. By utilizing LSTM networks and optimizing the forecasting models, energy efficiency could be enhanced, and energy consumption in buildings could be reduced.

This paper has significant implications for energy management and planning. The precise forecasting of electricity consumption can aid utility companies in optimizing their energy generation and distribution strategies, thereby fostering improved efficiency and cost savings. Additionally, the developed forecasting model holds promise in assisting policymakers in making informed decisions concerning energy policies and infrastructure development.

Conclusion

In summary, the novel technique demonstrated its effectiveness and robustness in predicting electricity usage. It could be used to forecast electricity consumption for short-term, medium-term, or long-term periods, depending on the specific requirements of the application. Moreover, it could revolutionize energy management practices and contribute to a more sustainable and efficient energy system.

The researchers acknowledged the limitations and challenges including data availability and quality, model complexity, generalization to other contexts, seasonal variations, dependency on historical data, resource constraints, and the impact of external factors. Moreover, for future work, they suggested utilizing larger datasets from various organizations to achieve more accurate results, improving algorithm accuracy through the use of different model optimizers, and developing methods for early detection and notification of unusual energy consumption patterns to prevent potential hazards.