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Adequacy of neural networks for widescale dayahead load forecasts on buildings and distribution systems using smart meter data
Energy Informatics volume 3, Article number: 28 (2020)
Abstract
Power system operation increasingly relies on numerous dayahead forecasts of local, disaggregated loads such as single buildings, microgrids and small distribution system areas. Various datadriven models can be effective predicting specific time series onestepahead. The aim of this work is to investigate the adequacy of neural network methodology for predicting the entire load curve dayahead and evaluate its performance for a widescale application on local loads. To do so, we adopt networks from other shortterm load forecasting problems for the multistep prediction. We evaluate various feedforward and recurrent neural network architectures drawing statistically relevant conclusions on a large sample of residential buildings. Our results suggest that neural network methodology might be illchosen when we predict numerous loads of different characteristics while manual setup is not possible. This article urges to consider other techniques that aim to substitute standardized load profiles using widescale smart meters data.
Introduction
Following the ongoing transformation of the European power system, it will be necessary to locally balance the increasing share of decentralized renewable energy supply. Distribution system operation will require a versatile and reliable model to obtain numerous dayahead load forecasts (DALF) on various levels of load aggregation. Widescale installation of smart meters allows to apply specialized datadriven techniques for local dayahead forecasting instead of currently used standardized load profiles (SLP). Among various machine learning approaches to load forecasting discussed in the literature, artificial neural networks (ANN) are the most intensively investigated methodology (Ahmad et al. 2018; Bourdeau et al. 2019).
There exist multiple models predicting a specific load time series onestepahead (e.g. hourly consumption of a university campus) (Ahmad et al. 2018). Nevertheless, only few proposals were made to use neural networks for dayahead (multistep) forecasts which will become more important for the distribution system operation. In particular, ANNs have been applied for predicting dayahead loads of larger buildings (Bagnasco et al. 2015; Chitsaz et al. 2015a; Ryu et al. 2016), consumer aggregations (Marinescu et al. 2013) and microgrids (Amjady et al. 2010; Hernández et al. 2014). Further, there have been attempts to forecast a single household load using novel deep learning techniques (Amarasinghe et al. 2017; Mocanu et al. 2016).
Proposed models allow only limited conclusions about applying ANN for DALF on a widescale^{Footnote 1}. The wellperforming setup required detailed knowledge of the load and was often found and manually tuned through trial and error. Further, the networks require large amounts of historic data and are subject to overfitting (Amarasinghe et al. 2017; Chalal et al. 2016). The accuracy substantially varied depending on the load. While a proposed model was accurate for a given building, similar setup performed notably worse when applied on a different consumer (Foucquier et al. 2013).
When deciding on a general methodology to substitute SLPs for widescale distribution system operation, statistical analysis is required to avoid casebased reasoning. In this work, we provide a comprehensive evaluation of the ANN methodology for local dayahead load forecasting. While many existing models forecast the timeseries onestepahead, we discuss various strategies to adopt a network for predicting the entire daily load curve. From the literature, we select and evaluate four different ANN architectures that appear suitable for a widescale application. We provide evidence that the prominent ANN methodology, as currently implemented, might be illsuited for predicting local consumption on a widescale, despite being accurate for particular loads.
Background
In this section, we provide theoretical background for our study. We start by describing the loads we will be predicting and continue with an overview of the ANN methodology as well as its state of the art applications for local load forecasting. The provided background allows unprepared readers to comprehend the selection and setup of ANN models that we describe in the subsequent section.
Local loads
Local loads range from single family homes to a small distribution system area supplied by an MV/LV substation including microgrids (DangHa et al. 2017). Naturally, their consumption is very diverse and more volatile than the transmission system load.
A consumption pattern mainly depends on the aggregation size. At a small level, it highly depends on the consumer behavior which makes it harder to predict. Generally, load is autoregressive and underlies annual and weekly seasonalities but to different extent depending on the size (Sevlian and Rajagopal 2014) (Fig. 1).
Artificial neural networks
Neural network methodology is a set of methods used in many machine learning applications. It can model almost any nonlinear relation between multiple inputs and outputs. Assuming existence, the relation is selflearned from historic data by a specialized training algorithm that solves the regression equation (Friedman et al. 2008)
where regression function r describes the systematic information about scalar variable y, and residual error ε represents all the uncaptured influences independent of vector input X.
A network can be seen as an interconnection of single elements called artificial neurons with an individual output
The n_{u} neuron inputs \(v^{(i)}\in \mathbb {R}\) weighted with \(w_{i}\in \mathbb {R}\) are processed by an activation function ϕ. If several of such neurons are interconnected into a network, theoretically, any nonlinear relationship can be approximated. For this, neurons are organized in layers and the data traverses the network from input to output passing through some neurons in the hidden layers one or several times.
We formalize an ANN as follows. Let \(\mathcal {N}\) be a neural network with n_{x} inputs, n_{y} outputs, and n_{w} interconnections that exists between the neurons. It is fully determined by its architecture, and the set of weights for each neuron. The network provides a regression function estimate
which maps a multivariate input \(X \in \mathbb {R}^{n_{x}}\) to the output \(Y \in \mathbb {R}^{n_{y}}\) for a given set of weights \(W = \left [w_{1},\hdots,w_{n_{w}}\right ] \in \mathbb {R}^{n_{w}}\) which are determined during the model training. To forecast with an ANN model we proceed as follows:
Architectures
We select a network architecture, relevant inputs and set hyperparameters that can not be learned directly from the data and are determined prior to training the network. A network architecture includes inputs, network type, number of neurons in the layers and their topology. While an extensive overview of different architectures contrasting the differences can be found in Raza and Khosravi (2015), there are two general types.
Feedforward neural network (FFNN) is the type, where starting at the input, the data traverses the network without any cycles or loops. The most prominent architecture of this type is the multilayer perceptron (MLP). It includes at least one hidden layer where each hidden neuron has a nonlinear activation function.
Recurrent neural networks (RNN) have feedback loops that allow data to traverse the network both ways. The connections between the neurons form a directed graph, and unlike a FFNN, a recurrent network has a dynamic internal state.
Deep neural networks (DNN) are those that have at least two hidden layers allowing complex topology both of FFNN and RNN types. It was shown, that increasing network size and complexity can often improve the accuracy, but elevates the time and historic data needed to train the network (Goodfellow et al. 2016).
Each network has a set of hyperparameters (number of neurons, activation function, training algorithm etc.) that significantly affect the prediction accuracy. The hyperparameters are often manually selected and iteratively finetuned given an indepth knowledge of the forecasting problem.
Network training
In the training phase, r needs to be estimated finding W that defines the network \(\mathcal {N}\) so that \(\mathbf {r}_{\mathcal {N}}\) yields the lowest error on the given training data and is expected to generalize well  i.e., produce the lowest error on the unseen data. The weights are initialized randomly and calculated with a supervised training algorithm using either a backpropagation (BP) of training error (Rumelhart et al. 1986) combined with different variants of gradient descent optimization (Kiefer and Wolfowitz 1952; Kingma and Ba 2017; Riedmiller and Braun 1993) or Levenberg Marquardt (LM) algorithm (Hagan and Menhaj 1994; Moré 1978).
Evaluation
At last, the forecast \(\hat {Y}\) is obtained with a previously trained network for a given input X^{∗} as \(\hat {Y} = \mathbf {r}_{\mathcal {N}} \left (X^{*}\right)\). The model is evaluated on test data using an appropriate error definition (Haben et al. 2014).
Setting hyperparameters and training often requires a vast amount of historic data, and it is hard to interpret the weights of the resulting network. Nonetheless, for some applications where a considerable amount of data is available, computation time is not an issue and there is an extensive problem knowledge allowing manual finetuning, ANN models can be very accurate. Inspired by the advances in machine learning, there are several applications for local load forecasting that we describe next.
Neural network applications
Numerous researchers applied the methodology described above to predict the load in microgrids (Hernández et al. 2014), distribution system areas (Wang et al. 2018) and buildings with a recent review citing over 90 publications (Runge and Zmeureanu 2019). The model performance depends fundamentally on the characteristics of time series. Thus, we focus on applications for shortterm forecasting of local loads with (sub)hourly resolution. This substantially reduces the number of related works to the ones summarized in Table 1.
The models have several aspects in common. They were developed for a specific load  a given building or an area of a distribution grid. The well performing architecture is setup manually, given explicit knowledge of the problem, researcher experience and intuition combined with trial and error. Model inputs are related to historical load, calendar features and, sometimes, daily weather. Further, they require large amounts of historic data. For instance, DNN model convolutional neural network (CNN) (Amarasinghe et al. 2017), restricted Boltzmann machine (RBM) (Mocanu et al. 2016; Ryu et al. 2016), long shortterm memory (LSTM) (Marino et al. 2016; Kong et al. 2017) and echostate network (ESN) (Shi et al. 2016)  all required years of data to setup and train the network.
Despite a common preconception, researchers are ambiguous about using weather related inputs such as outside ambient temperature (OAT) or solar irradiation. Some, do consider weather modeling electrical heating and PV at the level of MV/LV feeder (Hayes and Prodanovic 2016) or larger buildings (Pîrjan et al. 2017). An indepth sensitivity analysis can highlight an existing weather dependency (Llanos et al. 2012). Others, observe the models that do not use any weather data performing better for disaggregated loads (Hayes et al. 2015; Marinescu et al. 2013; Hernández et al. 2014). In Bagnasco et al. (2015), authors test two networks on the same dataset with and without such data. They observe no consistent advantage for either model. Alternatively, some researchers assume that the instantaneous weather changes do not affect the load significantly and consider only the month and the day arguing that the temperature does not change substantially from day to day (Hernández et al. 2014).
Hyperparameters have a major impact on the performance (Mena et al. 2014; Pîrjan et al. 2017). From the publications in Table 1 and more general review studies on ANN applications for load forecasting (Humeau et al. 2013; Runge and Zmeureanu 2019), we see that the models are usually setup and finetuned manually. Nevertheless, there have been attempts for automated trial and error (Amjady et al. 2010; Hernández et al. 2014).
Yet, despite the increasing interest, the development of fully automated models based on ANN is in a preliminary stage (Hutter et al. 2019). As of today, the load forecasting models rely on explicit a priori knowledge of the consumer and manual finetuning (Table 1). The well performing network architecture presented in the results is often found through a trial and error process and requires large amounts of historic data and computational resources
Methods
In this section, we formulate the dayahead forecasting problem and describe the model architectures, their setup and simulation. We will present the simulation results afterwards.
Dayahead forecasting requires to predict the entire curve for the next day. The exact time at which we need the predicted curve depends on the particular application. We assume that the forecast has to be done shortly before midnight for the upcoming day, as it is done in other studies (Table 1). We formulate the DALF problem as follows.
Given a training set\(\mathcal {T}: = \left \{ \left (X_{1},y_{1}\right),\hdots, \left (X_{m},y_{m}\right) \right \}\) that consists of m observations of multivariate input X={X_{1},X_{2},⋯ } and univariate output y={y_{1},y_{2},⋯ } time series, predict the vector
of n consecutive values representing the nextday load curve. With an input X_{d+1}, the prediction \(\hat {Y}_{d+1}(X_{d+1})\) has to minimize the root mean squared error (RMSE) defined as:
where \(\epsilon _{i} = y_{i}  \hat {y}_{i}\) is the residual error between actual y_{i} and predicted value \(\hat {y}_{i}\) according to (1).
To find \(\hat {Y}_{d+1}\) we use different model architectures described next in “Model architecture” section. Each model is setup as denoted in “Model setup” and evaluated through the simulation described in “Simulation” sections.
Model architecture
We use network architectures that are the most common among the load forecasting applications (Table 1). Studying the literature, we have not seen any fundamental reason why any of the networks is superior for certain types of loads, given appropriate setup and vigor at manual finetuning (Bourdeau et al. 2019). However, for widescale usage, practicality becomes important. Hence, we do not conisider DNN and other models that rely on abundant historic data or information from specific sensorial equipment (e.g. occupancy). We adopt two feedforward and two recurrent networks for dayahead prediction as described further in the text.
Dayahead prediction
In time series forecasting, we often encounter situations where only onestepahead prediction is required or considered. For electrical load, such task eventually corresponds to an intraday forecast. Following the scope of this work, we focus on dayahead forecasts, which are multistep predictions and require forecasting n consecutive points. While n depends on the time series resolution, there are three general strategies (Ben Taieb et al. 2012) to adopt a onestepahead forecasting model for a multistep prediction (Fig. 2).
Direct strategy is a straight forward approach that requires to setup and train n separate multiinput singleoutput (MISO) models with n_{x} inputs. However, such strategy disregards the dependencies between the predicted points of the curve. Moreover, it is computationally expensive, since we have to train n networks.
Multiout strategy sets up and trains one complex multipleinput multipleoutput (MIMO) network with n_{x} inputs and n outputs corresponding to the points of the forecast curve. It allows to consider the dependencies between the predicted timesteps and avoids the conditional independence assumption made by the direct strategy.
Recursive strategy forecasts onestepahead and uses the prediction \(\hat {y}_{1}\) as a new observation with which forecast is reiterated for i=2 and so on. The fundamental drawback is the sensitivity to prediction error, since it is accumulated while advancing the multistep forecast. Applying a recursive strategy will only be accurate if we have a good representation of the underlying time series generating process.
Feedforward models
A feed forward network can forecast dayahead load curve adopting either direct or multioutput strategy. Given input
we use the following multivariate forecasting models:

1
Multiple input single output multilayer perceptron (MISOMLP)

2
Multiple input multiple output multilayer perceptron (MIMOMLP)
In the first case, a separate MLP is trained to predict each point of Y_{d+1} (Fig. 2a). For the ease of exposition, we setup each network with same inputs and hyperparameters described further in the text. The training output data is split into n separate series y^{(1)},⋯,y^{(n)}, so that the n networks will have different weights after the training. The forecast output by the model consists of n separate predictions in
In the second case, we train one multioutput feedforward network with n_{x} inputs and n outputs. For a given X_{d+1}, the multivariate forecast is obtained as
with one MLP \(\mathbf {r}_{\mathcal {N}}\) to train (Fig. 2b).
Recurrent models
The concept of an RNN where past output values are fed back allows to create nonlinear autoregressive time series models. With it, we apply a recursive strategy to the multistep forecasting introducing the following models:

1
Nonlinear autoregressive model (NAR)

2
Nonlinear autoregressive model with exogenous inputs (NARX)
In the first case, we create a univariate autoregressive model with p lags defined as
where prediction \(\hat {y}_{i}\) is a a function of the p preceding values y_{i−1},…,y_{i−p} of the time series y (Fig. 2c).
In the second case, NARX model presents an extension of the NAR model that can consider external inputs to create a multivariate time series model
Here, a prediction \(\hat {y}_{i}\) is calculated as a function of its p lags \(y_{i1},\dots,y_{ip}\) and an exogenous input X_{i} (Fig. 2d).
Model setup
In Table 2, we summarize the networks used in this study with corresponding number of inputs, outputs, training data and degrees of freedom (total number of weights). As in related works, we assume hourly time series resolution (n=24). We now proceed explaining the setup and justifying the choice of hyperparameters.
Inputs
We consider the dependency on past values, OAT and weekly seasonality. For the feedforward models, we input the load curve of the previous day while NARX uses 24 lags (p=24) recursively. Further, multivariate networks (MISOMLP, MIMOMLP, NARX) model the seasonality and annual temperature cycle explicitly. The weekly seasonality is considered with a weekday number and a (boolean) public holiday variable. The annual temperature cycle is modeled as a function of month and day number. In this study, we assume that shortterm weather changes do not notably impact the load (Hernández et al. 2014). As we will see, the simulation results validate this assumption.
The univariate model (NAR) considers weekly patterns implicitly by using one week of lags (p=168). The annual cycle is accounted for by using only two most recent months of data for training that is repeated monthly.
Network training is more efficient with normalized inputs(Dan Foresee and Hagan 1997). Following a standard practice, we apply minmaxnormalization as follows. Each separate input x^{(j)}, with j∈[1,⋯,n_{x}] is transformed to
where the smallest input value \(x^{(j)}_{\text {min}}\) corresponds to \(\tilde {x}^{(j)} = 1\) and the largest input \(x^{(j)}_{\text {max}}\) corresponds to \(\tilde {x}^{(j)}=1\). Herewith, every normalized training input lies in the range of [−1;1]. We use the same normalization constants \(x^{(j)}_{\text {min}}, x^{(j)}_{\text {max}}\) for the evaluation.
Outputs
Subsequently, network output must be transformed back into the range of original data. We provide the network with a preprocessing block that appears before the input layer and a postprocessing block that appears after the output layer (Fig. 3)
Additionally, for recursive models (NAR, NARX) postprocessing includes a hold and release unit. In their case, the output \(\hat {Y}\) is a scalar prediction for a single time step. For those models, postprocessing allows to output the entire forecast curve at once.
Training data
For widescale prediction of local loads it is important to rely on the smallest viable amount of training data. In addition to the availability issue, time series can constantly alter their regime with old data becoming irrelevant (inhabitants change, new equipment is installed). Multivariate networks (MIMOMLP, MISOMLP, NARX) require at least one year of data to learn the dependency on the month. Currently used SLPs also require total consumption over a year to scale the profile. In contrast, the NAR model is trained only on the two months of preceding data.
Hyperparameters
Activation function of the neurons must correspond to the applied normalization. With x^{(j)}∈[−1;1], each layer should have ϕ(u) with the same domain and range. Unfortunately, the majority of related works (Table 1) do not specify the activation function (Amjady et al. 2010; Hernández et al. 2014; Hayes and Prodanovic 2016; Marinescu et al. 2013; Mocanu et al. 2016; Pîrjan et al. 2017; Marino et al. 2016). Among the rest, the most common functions are linear (Amarasinghe et al. 2017; Llanos et al. 2012; Mena et al. 2014; Ryu et al. 2016) and tanhsigmoid (Bagnasco et al. 2015; Shi et al. 2016; Kong et al. 2017) defined as
and which we use in this study since \(\phi (u) \in [1;1], \forall u\in \mathbb {R}\).
Network size determines the predictive capacity of the model. For this work, we use networks with one hidden layer consisting of 15 neurons. The optimal number of hidden layers and neurons depends on the task and there is no theoretical methodology to determine it. Often, the size is chosen using experience in comparable problems. In related works, best performing networks also had one hidden layer with a similar number of neurons (Table 1).
Training algorithm for each network combines LM method with Bayesian regularization (Dan Foresee and Hagan 1997). The LM algorithm appears to be much faster than BPbased approaches for moderatelysized networks (Hagan and Menhaj 1994). Bayesian regularization does not keep some of the limited training data as a validation set in contrast to the commonly used early stopping technique (Friedman et al. 2008). At the same time, it is effective preventing overfitting and improving generalization of moderately oversized networks (Dias et al. 2003).
Simulation
We conduct the following simulations predicting the load day by day and calculating RMSE for each predicted time series. The loads are taken from the publicly available smart meter data of Irish Commission for Energy Regulation (Commission for Energy Regulation (CER) 2012). Given a sample of over 900 homes^{Footnote 2} at the same location, we select the loads with no missing data and annual consumption within the interquartile range (IQR). The resulting dataset consists of 444 single buildings with 17 consecutive months of data. Each time series was resampled equidistantly with a 60 minute resolution and normalized by its maximum value to allow scalefree comparisons between the loads. We conduct the simulations using MATLABsoftware. The hyperparameters of the networks not mentioned explicitly in “Model setup” section were left to MATLAB defaults.
Simulation A We create aggregated loads of different size for which we predict the demand curves over one month (August). For each load, we train a network on preceding data 100 times to investigate the effect that random weights initialization has on the training solution. This gives us a sample of 100 forecast errors for each load and model.
Simulation B We predict the loads of single homes separately during another five months period (September – December). We retrain the networks every month using the preceding data for training on a rolling basis.
Results
We have observed substantial stochastic dispersion of the forecast error referable to random weight initialization (“Forecasting loads of different size” section) and variation among the households (“Forecasting large sample of households” section). Error distri bution was represented with violinplots while boxplots helped us to evaluate the range and statistical significance (pvalue 0.05). Irish residential SLPs were used as a reference forecast (Irish standard load profiles 2014). Stochastic variation of errors had notable implications described in this section and discussed in the end of the article.
Forecasting loads of different size
We observed that mean RMSE changed drastically with load size and had notable dispersion. Average forecast of the biggest aggregation was three to four times as accurate as for a single home (Table 3). Further, forecast error had variation between 9% and 62% in terms of relative range (Fig. 4).
The ANN models were setup adequately. Residuals were mostly uncorrelated and according to Eq. 1 contained no systematic information (Fig. 5 left column). Uncaptured influence of a further variable would have been manifested in a substantial correlation which only started to appear for bigger loads (Fig. 5 right column). We attribute it to weather that began to have a notable effect on the overall consumption.
On the other side, the training algorithm failed to provide stable solutions. The dispersion could not be explained by the lack of historic data and was observed for each network. The most consistent training was achieved for the NAR model that is underdetermined with twice as many degrees of freedom as data (approx. 2:1). Yet, welldetermined NARX (approx. 1:19) had wider IQR. In some cases, outliers indicating eventual overfitting were observed.
Only for the biggest load was an ANN forecast significantly more accurate than the reference (Fig. 4). Further, we observed the direct approach being the worst among multistep strategies. Apart from this, we could not make any consistent comparisons between the architectures. While RNNs were accurate for smaller loads, MIMOMLP delivered smaller error for bigger loads.
Due to the error dispersion, any meaningful conclusion can only be made using statistics on a relevantly large sample of trained networks. Our results indicated that the applied networks are only accurate for bigger aggregations (400 homes onwards) while for smaller loads they failed to infer the inherent patterns.
Forecasting large sample of households
We observed that the forecast error varies substantially depending on the given household (Fig. 6). The relative range spread between 162% (MISOMLP) and 127% (NARX model) among the sample notwithstanding the homes had similar annual consumption and location.
On average, none of the networks reached the reference accuracy (SLP). As in the previous simulation, direct forecast (MISOMLP) was observed to be least accurate among multistep strategies while recursive prediction with RNN was the most successful (NARX, NAR). Nevertheless, while for some households NAR substantially improved the accuracy by up to 25% against the reference, such result was only valid for the specific load. Still, it had mean error that was 10% above the reference applied on the same sample of households.
Discussion
Our results suggest that ANN methodology might be inapt for widescale local load forecasting. Each of the four architectures produced dispersed forecast errors linked to the variation among training solutions and simulated buildings. As expected, mean error rapidly decreased for bigger loads. At the same time, no architecture was consistently better for each size. Even more surprisingly, no network was significantly more accurate for smaller loads than the forecast with an SLP (benchmark).
It is known that network performance depends on the characteristics of the time series linked to the load size and that ANNs do deliver accurate forecasts for some regular load curves (Wang et al. 2018). These models are often setup manually for onestepahead predictions using extensive training data (Runge and Zmeureanu 2019). To the best of our knowledge, ANNs were never applied in context of widescale dayahead predictions on a statistically relevant sample of local loads where historic data is limited, manual adjustment is not possible and the loads can be highly volatile.
We have observed that no network architecture evaluated in this work was decisively superior for all loads (Fig. 4). Moreover, only for the biggest aggregation, there was a network significantly better than the benchmark. While this contradicts many specific cases where similar ANN models were shown to perform well (Table 1), a comparable conclusion has been reported earlier for small loads where even a naive approach outperformed an MLP (Hayes et al. 2015).
The accuracy of an ANNmodel for wide range of loads can be improved by selecting the best architecture among a set of candidates for each given consumer. Such idea of an ensemble model is currently discussed (Ahmad et al. 2018; Bourdeau et al. 2019). However, we have observed notable dispersion of the forecast error obtained by the same network due to the random weight initialization which is a fundamental part of network training. This dispersion makes model selection based on trial and error (Hernández et al. 2014; Amjady et al. 2010) and comparison in general, more difficult requiring to consider a statistically relevant sample of training solutions for each candidate.
Our results suggest further practical conclusions for using ANN methodology to forecast local loads dayahead. Firstly, onestepahead models, which are more common, should not be adopted directly. Predicting each point of the load curve separately was significantly worse than any other strategy described in this work (Fig. 2).
Secondly, a more complex network architecture with additional input and training data (e.g., DNN) is unlikely to be substantially more accurate. The residual analysis shows that our simple setups had sufficient modeling capacity (Fig. 5). It also shows that, for smaller loads, no further inputs are required and it is enough to consider only annual temperature cycle instead of daily weather changes. Notwithstanding some special cases (e.g. substantial PV share), this counterintuitive observation is consistent with other studies (Hayes et al. 2015; Marinescu et al. 2013; Hernández et al. 2014) and contrasts to the transmission system level where weather explains up to 70% of load variation (DangHa et al. 2017).
Most importantly, stochastic nature of the results becomes apparent when predicting numerous local loads. The variation among the loads and training solutions urges to consider statistical significance of any result. We have demonstrated substantial relative range of forecast error which undermines any conclusion based uniquely on mean error and such like. Related studies rarely consider confidence intervals of errors when evaluating an ANN model applied for local load forecasting (Table 1). Our statistical analysis shows that, even when aptly setup, ANNs may fail to reach the accuracy of currently used SLPs (Fig. 6).
We explain the weak performance of applied architectures by nonstationarity of local loads. Following the ANN methodology, a trained network forecasts unseen data, assuming that the statistical properties of the process generating the data remain constant. Once the regression function is estimated, a network does not adapt to the change in data characteristics which often arrises with local loads. In such case, historic data quickly becomes irrelevant undermining the network training. The fact that the most accurate model used the least amount of training data supports this hypothesis (NAR model using only 2 months of data).
Stationarity assumption is central for any neural network architecture. Hence, it is unclear how ANN methodology, in general, can become effective predicting local loads on a widescale. Neural networks are known for their blackbox character and it is hard to formulate a theory about their limitations for the given problem. In this situation, empirical evidence obtained through statistical analysis, as in this work, becomes important when deciding on a general approach for replacing SLPs. As currently applied, ANN methodology might be illchosen for widescale forecasting of local loads. Future research should focus on adaptive models (Ditzler et al. 2015; Kuznetsov and Mohri 2015) which may require an entirely different approach, despite ANNs being successful for other machine learning problems.
Conclusion
This work investigated neural network methodology for widescale dayahead forecasting of local loads such as single homes and small aggregations encountered in microgrids or distribution system areas in general. Currently, grid operation relies on standardized profiles for such forecasts which fail to reflect the volatility and diversity of the loads as they become of interest for local energy management. From numerous existing network architectures, we identify and apply several setups that are practical for widescale dayahead prediction. As exist at present, ANNs do not yield any statistically significant improvement. Herewith, we provide empirical evidence that a prominent neural network methodology might be inadequate for widescale dayahead forecasts of local loads.
Availability of data and materials
Smart meter data set with measured load curves of numerous single consumers is publicly available from Irish Commission for Energy Regulation (Commission for Energy Regulation (CER) 2012).
Notes
Widescale application implies predicting separately numerous loads of different size and characteristics ranging from single buildings to distribution system areas.
We consider only homes of the control group. Other homes in this dataset participated in a trial that affected their consumption.
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Oleg Valgaev conducted the literature review, simulations and drafted the manuscript. Hartmut Schmeck and Friederich Kupzog edited, read and approved the final manuscript.
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Valgaev, O., Kupzog, F. & Schmeck, H. Adequacy of neural networks for widescale dayahead load forecasts on buildings and distribution systems using smart meter data. Energy Inform 3, 28 (2020). https://doi.org/10.1186/s42162020001326
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DOI: https://doi.org/10.1186/s42162020001326
Keywords
 Smart grid
 Machine learning
 Load forecasting
 Dayahead
 Neural network
 Local load
 Buildings
 Microgrids
 Distribution system