Energy load forecasting across multiple buildings is beneficial for energy saving. Currently, most methodologies are training a single global model for all buildings as the deep learning model relies on large-scale data. However, the energy data distribution may vary a lot across different buildings and enforcing a global model may cause unnecessary computing resource overutilization. Meanwhile, building energy management encounters repeated manual efforts for machine learning model training over the new sensor data. To improve the computing resource utilization of load forecasting model training and automation of building energy management, a new automatic learning framework is proposed to support automatic building energy data analytics. The machine learning model is customized for each building based on an automatic algorithm with efficient model evaluations. The new framework brings comparable performance to federated energy data learning while fewer computing resource is consumed.
KEYWORDS: Deep learning, Data modeling, Transformers, Education and training, Machine learning, Neural networks, Performance modeling, Visualization, Data privacy, Autoregressive models
Building energy consumption grows rapidly with modern urbanization while the buildings’ sensor data also increases explosively. Improving energy utilization of community buildings is critical for sustainable development and global climate challenge. However, the data isolation across buildings’ privacy management prevents largescale machine learning model training, which may reduce the prediction accuracy due to lack of data. Federated building energy learning supports distributed learning through model sharing so that data privacy is mitigated. In federated learning, model-sharing brings a new concern about network resource limitation. Deep learning model transfers across multiple buildings would cause network ingestion and incur high latency of federated training. To improve the efficiency of federated training with fewer resources, a new federated learning algorithm is proposed with a new deep learning model design. The deep learning model memory usage is reduced by 80% while energy load forecasting accuracy is still comparable to the state-of-the-art methods.
KEYWORDS: Data modeling, Deep learning, Education and training, Transformers, Machine learning, Buildings, Performance modeling, Power consumption, Neural networks, Design and modelling
Electricity data sensors are widely used across large buildings and households. As the data is collected by distributed sensors from varied locations, privacy-preserving becomes a top concern for data owners. Meanwhile, multiple deep learning models achieved state-of-art performance on forecasting with the electricity time series data in a centralized training mechanism. Although these deep learning models are powerful at capturing temporal features and making precise predictions, it usually consumes a large amount of memory and resources during the training process. To address two problems, i.e., the data privacy issue and high-demanded resources for training, we propose an efficient and practical deep learning model using a transformer framework while utilizing federated learning to move the training on local data instead of on a centralized place. With the proposed deep learning model, the computation will reduce its memory usage by 60% while achieving similar and even better results on forecasting with the electricity time series data. Case studies on the university communities’ building demonstrate our proposed solution’s great potential and comparative performance compared to the state of the arts.
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