A Time Series is a sequence of data points, typically ordered chronologically, where each data point represents a measurement or observation recorded at a specific time or over a sequence of time intervals. Time series data is used to analyze how a variable changes over time, and it is a fundamental concept in various fields, including finance, economics, signal processing, environmental science, and more.

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Battery Degradation Temporal Modeling Using LSTM Networks

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Accurate modeling of battery capacity degradation is an important component for both battery manufacturers and energy management systems. In this paper, we develop a battery degradation model using deep learning algorithms. The model is trained with the real data collected from battery storage solutions installed and operated for behind-the-meter customers. In the dataset, battery operation data are recorded at a small scale (five minutes) and battery capacity is measured at every six months. In order to improve the training performance, we apply two preprocessing techniques, namely subsampling and feature extraction on operation data, and also interpolating between capacity measurements at times for which battery operation features are available. We integrate both cyclic and calendar aging processes in a unified framework by extracting the corresponding features from operation data. The proposed model uses LSTM units followed by a fully-connected network to process weekly battery operation features and predicts the capacity degradation. The experimental results show that our method can accurately predict the capacity fading and significantly outperforms baseline models including persistence and autoregressive (AR) models.

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Conditioning Neural Networks: A Case Study of Electrical Load Forecasting

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Machine learning tasks typically involve minimizing a loss function that measures the distance of the model output and the ground-truth. In some applications, in addition to the usual loss function, the output must also satisfy certain requirements for further processing. We call such requirements model conditioning. We investigate cases where the conditioner is not differentiable or cannot be expressed in closed form and, hence, cannot be directly included in the loss function of the machine learning model. We propose to replace the conditioner with a learned dummy model which is applied on the output of the main model. The entire model, composed of the main and dummy models, is trained end-to-end. Throughout training, the dummy model learns to approximate the conditioner and, thus, forces the main model to generate outputs that satisfy the specified requirements. We demonstrate our approach on a use-case of demand charge-aware electricity load forecasting. We show that jointly minimizing the error in forecast load and its demand charge threshold results in significant improvement to existing load forecast methods.

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