Artificial neural networks and other machine learning methods

A common feature of the above analyses was that reasonable control of the data sources was maintained, either by focusing on a single building, generating data by simulation or collecting data from a specific set of buildings. From building energy theory (Thomas 2006; Goričanec 2009; Ward 2009), the relationships between factors such as fabric performance, glazing ratio, aspect ratio, orientation and shading are known to be sophisticated. Taken together for real buildings, it is therefore expected that the relationships between these factors and actual building energy use would be complex and non-linear.

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Figure 2.2 Example architecture of an artificial neural network (ANN) by author

An appropriate method for analysis such relationships would appear to be the artificial neural network (ANN) model. An artificial neural network (as depicted in Figure 2.2) is a machine-learning method that adopts some principles similar to the function of biological neural networks. The ANN comprises the following key elements (Fausett 1994; Ripley 1996):

Input neurons Neurons that take values representing the information presented to the network.

Output neurons Neurons that give values estimated by the network.

Hidden neurons Neurons that sit in one or more hidden layers between the input and output neurons and allow intermediate processing. Hidden layers are optional depending on the application.

Input layer neurons

Hidden layer neurons

Output layer neurons Connection

weights Connection

weights

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weights

Connections linking upstream and downstream neurons (e.g. input to hidden, hidden to output) have a corresponding weight which influences the value taken by downstream neuron. The connection weights are adjusted as the network is trained.

To train the network, training patterns are presented to the ANN for which there are known outputs (target values) for given inputs. The ANN outputs an estimate value from which the output error – the difference between the estimate and target value – is determined and used to adjust the connection weights. Separate validation patterns are applied periodically during training to measure the network’s prediction performance at that point and to determine the point to stop training. The performance of the network at this point is measured in terms of its generalisation error which is the aggregate error in the estimation of outputs for patterns on which the ANN has not been trained.

Many common types of artificial neural networks, such as the multi-layer perceptron (MLP) model shown in Figure 2.1 have similarities to statistical methods in terms of exploring underlying distributions. Reported advantages offered by artificial neural networks over statistical methods are the ability to avoid overfitting to training data (using stopped training algorithms), shorter computational times and better learning of moderately pathological functions where training data may be noisy (Sarle 2002a). Artificial neural networks would appear to be an appropriate an application for evaluating energy determinants of buildings as an alternative to statistical methods.

Aydinalp et al. (2002) noted that artificial neural networks (ANNs) were originally considered for power analysis around the early 1990s when they were used in utility load forecasting, for example relating weather conditions to make short term loading on a particular electrical substation. Soon after, studies commenced on the use of ANNs for energy use forecasting in single buildings. Aydinalp et al. cited studies carried out by Kreider throughout the 1990s; these ranged from making short term predictions on electricity use based on historical data to applying ANNs for forecasting energy savings

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for particular buildings through building retrofits. Dong et al. (2005) considered the use of support vector machines (SVMs) for building load forecasting, highlighting their benefits over ANNs in that they have a unique solution and there are fewer variables to optimise. More recently, Jetcheva et al.

(2014) reported advancements in the day-ahead electrical load forecasting using ANNs.

Yalcintas (2008) determined energy savings retrospectively by comparing post-retrofit energy use with energy use projected from a pre-retrofit condition using a trained ANN. The ANN had been trained to predict the energy performance of the building services systems based on external weather factors.

Relatively strong prediction performance was achieved with mean absolute percentage errors (MAPE) below 10%.

Studies carried out by Aydinalp et al. (2002; 2004) considered training ANNs to forecast individual energy uses – appliances, lighting, space cooling, space and domestic hot water heating – for residential buildings previously unseen, based on an extensive number of inputs. The training data was derived from a Canadian household energy use survey with up to 1,228 sets used for training and validation depending on the end use type. Inputs included types and number of appliances, lighting, number of occupants and household income. Outputs were annual energy use in kWh. A variety of network architectures, learning rates, training algorithms and activation functions were assessed. The optimised ANN achieved CV-RMSE values lower than 2% for space heating and lower than 3% for the others. This performance was shown to be better than an equivalent engineering method.

Other examples of machine learning methods being applied in the field of building energy use include, improving the control of HVAC systems (Huang et al. 2015b; Huang et al. 2015a), prioritising the selection of building energy efficiency measures (Karmellos et al. 2015) and using ANNs to improve the accuracy of simple (‘surrogate’) building models used for energy labelling purposes (Melo et al.

2014).

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Although not specific to embodied carbon impacts in buildings, similar studies have also been carried out on the use of ANNs for Life Cycle Assessment (LCA) studies to inform designers on product energy use when selecting product attributes. Seo et al. (2005) used decision trees to initially categorise household products based on parameters such as their durability and mass. This categorisation would be used to select the ANN to apply to forecast life-cycle energy impact of the product based on other attributes. The results found generalisation error to vary between 0.1 to 12% depending on the product; this was considered to be satisfactory for using the model as an options appraisal method.

As shown, there are many examples of machine learning methods being applied in the broad context of building energy use. However, there does not appear to be evidence of such methods being tested to relate building parameters, such as form factors and system efficiency, to actual operational energy use, particularly for non-domestic buildings and in the higher education sector.