We propose a method for detecting and forecasting events of high energy demand, which are managed at the national level in demand side response programmes, such as the UK Triads. The methodology consists of two stages: load forecasting with long short-term memory neural network and dynamic filtering of the potential highest electricity demand peaks by using the exponential moving average. The methodology is validated on real data of a UK building management system case study. We demonstrate successful forecasts of Triad events with RRMSE ≈ 2.2% and MAPE ≈ 1.6% and general applicability of the methodology for demand side response programme management, with reduction of energy consumption and indirect carbon emissions.

中文翻译：

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使用长短期记忆神经网络对能源需求响应进行建模

我们提出了一种检测和预测高能需求事件的方法，该方法在需求方面的响应计划（例如UK Triads）中在国家一级进行管理。该方法包括两个阶段：使用长期短期记忆神经网络进行负荷预测，以及使用指数移动平均值对潜在的最高用电需求峰值进行动态过滤。该方法论已在英国建筑管理系统案例研究的真实数据中得到验证。我们证明与三合会活动的成功预测[R [R中号小号é ≈2.2 ％和中号一个P é ≈1.6 ％ 以及需求方响应计划管理方法论的普遍适用性，同时减少了能源消耗和间接碳排放。