Smart Buildings

Hybrid Deep Learning Promises to Better Predict Building Energy Consumption

Accurate forecasting of building energy consumption is as valuable as it is complicated. The ability to reliably predict how much energy a building will use in the next hour, week, or year would bring about a new level of flexibility within and between our smart buildings but a range of internal and external variables have presented a barrier to such development. Recently, however, an international research collaboration has published the results of a new approach that appears to trigger an accuracy-evolution in building energy management. The paper, published in IEEE journal, proposes a “novel hybrid deep learning prediction approach” that utilizes long short-term memory as an encoder and gated recurrent unit as a decoder in conjunction with the Algebra of Communicating Processes or “ACP theory” — an algebraic approach to reasoning about concurrent systems. The new approach has been tested and validated against real-world datasets and consistently outperformed traditional predictive models, promising a new era […]

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