Abstract
Traditional power generating technologies rely on fossil fuels, which contribute to worldwide environmental issues such as global warming and climate change. As a result, renewable energy sources (RESs) are used for power generation where battery energy storage systems (BESSs) are widely used to store electrical energy for backup, match power consumption and generation during peak hours, and promote energy efficiency in a pollution-free environment. Accurate battery state of health (SOH) prediction is critical because it plays a key role in ensuring battery safety, lowering maintenance costs, and reducing BESS inconsistencies. The precise power consumption forecasting is critical for preventing power shortage and oversupply, and the complicated physicochemical features of batteries dilapidation cannot be directly acquired. Therefore, in this paper, a novel hybrid architecture called ‘CL-Net’ based on convolutional long short-term memory (ConvLSTM) and long short-term memory (LSTM) is proposed for multi-step SOH and power consumption forecasting. First, battery SOH and power consumption-related raw data are collected and passed through a preprocessing step for data cleansing. Second, the processed data are fed into ConvLSTM layers, which extract spatiotemporal features and form their encoded maps. Third, LSTM layers are used to decode the encoded features and pass them to fully connected layers for final multi-step forecasting. Finally, a comprehensive ablation study is conducted on several combinations of sequential learning models using three different time series datasets, i.e., national aeronautics and space administration (NASA) battery, individual household electric power consumption (IHEPC), and domestic energy management system (DEMS). The proposed CL-Net architecture reduces root mean squared error (RMSE) up to 0.13 and 0.0052 on the NASA battery and IHEPC datasets, respectively, compared to the state-of-the-arts. These experimental results show that the proposed architecture can provide robust and accurate SOH and power consumption forecasting compared to the state-of-the-art.
Highlights
IntroductionMost energy is provided to the consumers by fossil fuel-based power plants globally
Most energy is provided to the consumers by fossil fuel-based power plants globally.these power plants have some problems, such as they depend on non-renewable resources
The proposed architecture is implemented in Python language (Version 3.8.5) using the most popular deep learning (DL) framework Keras (Version 2.5.0) with TensorFlow (Version 2.5.0) at the backend
Summary
Most energy is provided to the consumers by fossil fuel-based power plants globally. These power plants have some problems, such as they depend on non-renewable resources. The energy produced by fossil fuel power plants should be reduced and should be moved toward renewable forms of energy [1]. It is always a big challenge for smart grids to match energy production and its consumption every time. If batteries are charged based on the highest power peak and not the actual power consumed, the energy bill can be significantly reduced by adding BESS. Batteries in a BESS can be charged with excess solar power, and when the sun goes down or is blocked, load can be utilized from these batteries
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