5 research outputs found
Data-driven building energy efficiency prediction based on envelope heat losses using physics-informed neural networks
The analytical prediction of building energy performance in residential
buildings based on the heat losses of its individual envelope components is a
challenging task. It is worth noting that this field is still in its infancy,
with relatively limited research conducted in this specific area to date,
especially when it comes for data-driven approaches. In this paper we introduce
a novel physics-informed neural network model for addressing this problem.
Through the employment of unexposed datasets that encompass general building
information, audited characteristics, and heating energy consumption, we feed
the deep learning model with general building information, while the model's
output consists of the structural components and several thermal properties
that are in fact the basic elements of an energy performance certificate (EPC).
On top of this neural network, a function, based on physics equations,
calculates the energy consumption of the building based on heat losses and
enhances the loss function of the deep learning model. This methodology is
tested on a real case study for 256 buildings located in Riga, Latvia. Our
investigation comes up with promising results in terms of prediction accuracy,
paving the way for automated, and data-driven energy efficiency performance
prediction based on basic properties of the building, contrary to exhaustive
energy efficiency audits led by humans, which are the current status quo.Comment: 8 pages, 1 figur
Transfer learning for day-ahead load forecasting: a case study on European national electricity demand time series
Short-term load forecasting (STLF) is crucial for the daily operation of
power grids. However, the non-linearity, non-stationarity, and randomness
characterizing electricity demand time series renders STLF a challenging task.
Various forecasting approaches have been proposed for improving STLF, including
neural network (NN) models which are trained using data from multiple
electricity demand series that may not necessary include the target series. In
the present study, we investigate the performance of this special case of STLF,
called transfer learning (TL), by considering a set of 27 time series that
represent the national day-ahead electricity demand of indicative European
countries. We employ a popular and easy-to-implement NN model and perform a
clustering analysis to identify similar patterns among the series and assist
TL. In this context, two different TL approaches, with and without the
clustering step, are compiled and compared against each other as well as a
typical NN training setup. Our results demonstrate that TL can outperform the
conventional approach, especially when clustering techniques are considered
DeepTSF: Codeless machine learning operations for time series forecasting
This paper presents DeepTSF, a comprehensive machine learning operations
(MLOps) framework aiming to innovate time series forecasting through workflow
automation and codeless modeling. DeepTSF automates key aspects of the ML
lifecycle, making it an ideal tool for data scientists and MLops engineers
engaged in machine learning (ML) and deep learning (DL)-based forecasting.
DeepTSF empowers users with a robust and user-friendly solution, while it is
designed to seamlessly integrate with existing data analysis workflows,
providing enhanced productivity and compatibility. The framework offers a
front-end user interface (UI) suitable for data scientists, as well as other
higher-level stakeholders, enabling comprehensive understanding through
insightful visualizations and evaluation metrics. DeepTSF also prioritizes
security through identity management and access authorization mechanisms. The
application of DeepTSF in real-life use cases of the I-NERGY project has
already proven DeepTSF's efficacy in DL-based load forecasting, showcasing its
significant added value in the electrical power and energy systems domain
Targeted demand response for flexible energy communities using clustering techniques
The present study proposes clustering techniques for designing demand
response (DR) programs for commercial and residential prosumers. The goal is to
alter the consumption behavior of the prosumers within a distributed energy
community in Italy. This aggregation aims to: a) minimize the reverse power
flow at the primary substation, occuring when generation from solar panels in
the local grid exceeds consumption, and b) shift the system wide peak demand,
that typically occurs during late afternoon. Regarding the clustering stage, we
consider daily prosumer load profiles and divide them across the extracted
clusters. Three popular machine learning algorithms are employed, namely
k-means, k-medoids and agglomerative clustering. We evaluate the methods using
multiple metrics including a novel metric proposed within this study, namely
peak performance score (PPS). The k-means algorithm with dynamic time warping
distance considering 14 clusters exhibits the highest performance with a PPS of
0.689. Subsequently, we analyze each extracted cluster with respect to load
shape, entropy, and load types. These characteristics are used to distinguish
the clusters that have the potential to serve the optimization objectives by
matching them to proper DR schemes including time of use, critical peak
pricing, and real-time pricing. Our results confirm the effectiveness of the
proposed clustering algorithm in generating meaningful flexibility clusters,
while the derived DR pricing policy encourages consumption during off-peak
hours. The developed methodology is robust to the low availability and quality
of training datasets and can be used by aggregator companies for segmenting
energy communities and developing personalized DR policies
A comparative assessment of deep learning models for day-ahead load forecasting: Investigating key accuracy drivers
Short-term load forecasting (STLF) is vital for the effective and economic
operation of power grids and energy markets. However, the non-linearity and
non-stationarity of electricity demand as well as its dependency on various
external factors renders STLF a challenging task. To that end, several deep
learning models have been proposed in the literature for STLF, reporting
promising results. In order to evaluate the accuracy of said models in
day-ahead forecasting settings, in this paper we focus on the national net
aggregated STLF of Portugal and conduct a comparative study considering a set
of indicative, well-established deep autoregressive models, namely multi-layer
perceptrons (MLP), long short-term memory networks (LSTM), neural basis
expansion coefficient analysis (N-BEATS), temporal convolutional networks
(TCN), and temporal fusion transformers (TFT). Moreover, we identify factors
that significantly affect the demand and investigate their impact on the
accuracy of each model. Our results suggest that N-BEATS consistently
outperforms the rest of the examined models. MLP follows, providing further
evidence towards the use of feed-forward networks over relatively more
sophisticated architectures. Finally, certain calendar and weather features
like the hour of the day and the temperature are identified as key accuracy
drivers, providing insights regarding the forecasting approach that should be
used per case.Comment: Keywords: Short-Term Load Forecasting, Deep Learning, Ensemble,
N-BEATS, Temporal Convolution, Forecasting Accurac