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 […]