Purpose

This paper aims to develop a forecasting tool for the automatic assessment of both environmental and economic benefits resulting from low-carbon investments in the real estate sector, especially when applied in large building stocks. A set of four artificial neural networks (NNs) is created to provide a fast and reliable estimate of the energy consumption in buildings due to heating, hot water, cooling and electricity, depending on some specific buildings’ characteristics, such as geometry, orientation, climate or technologies.

Design/methodology/approach

The assessment of the building’s energy demand is performed comparing the as-is status (pre-retrofit) against the design option (post-retrofit). The authors associate with the retrofit investment the energy saved per year, and the net monetary saving obtained over the whole cost after a predetermined timeframe. The authors used a NN approach, which is able to forecast the buildings’ energy demand due to heating, hot water, cooling and electricity, both in the as-is and in the design stages. The design stage is the result of a multiple attribute optimization process.

Findings

The approach here developed offers the opportunity to manage energy retrofit interventions on wide property portfolios, where it is necessary to handle simultaneously a large number of buildings without it being technically feasible to achieve a very detailed level of analysis for every property of a large portfolio.

Originality/value

Among the major accomplishments of this research, there is the creation of a methodology that is not excessively data demanding: the collection of data for building energy simulations is, in fact, extremely time-consuming and expensive, and this NN model may help in overcoming this problem. Another important result achieved in this study is the flexibility of the model developed. The case study the authors analysed was referred to one specific stock, but the results obtained have a more widespread importance because it ends up being only a matter of input-data entering, while the model is perfectly exportable in other contexts.

中文翻译：

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低碳投资中的自动能源需求评估：建筑组合的神经网络方法

目的

本文旨在开发一种预测工具，用于自动评估房地产领域的低碳投资（特别是在大型建筑存量中）所产生的环境和经济效益。创建了一组四个人工神经网络（NN），以根据建筑物的某些特定特征（例如几何形状，方向，气候或技术。

设计/方法/方法

对建筑物的能源需求进行评估，将现状（改造前）与设计方案（改造后）进行比较。作者将每年节省的能源与改造投资相关联，并在预定的时间范围内在整个成本中节省了净金钱。作者使用了一种NN方法，该方法能够在现状和设计阶段预测由于供暖，热水，冷却和电力导致的建筑物能源需求。设计阶段是多属性优化过程的结果。

发现

这里开发的方法为管理广泛的房地产投资组合中的能源干预措施提供了机会，在这种情况下，有必要同时处理大量建筑物，而在技术上不可能对大型投资组合的每个属性进行非常详细的分析。

创意/价值

在这项研究的主要成就中，创造了一种对数据没有过多要求的方法：事实上，用于建筑能耗模拟的数据收集非常耗时且昂贵，并且这种NN模型可能有助于克服这个问题。这项研究获得的另一个重要结果是所开发模型的灵活性。作者分析的案例研究涉及一种特定的股票，但是获得的结果具有更广泛的重要性，因为最终它仅是输入数据输入的问题，而该模型在其他情况下也可以完美导出。