17,143 research outputs found
Day-Ahead Load Demand Forecasting in Urban Community Cluster Microgrids Using Machine Learning Methods
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).The modern-day urban energy sector possesses the integrated operation of various microgrids located in a vicinity, named cluster microgrids, which helps to reduce the utility grid burden. However, these cluster microgrids require a precise electric load projection to manage the operations, as the integrated operation of multiple microgrids leads to dynamic load demand. Thus, load forecasting is a complicated operation that requires more than statistical methods. There are different machine learning methods available in the literature that are applied to single microgrid cases. In this line, the cluster microgrids concept is a new application, which is very limitedly discussed in the literature. Thus, to identify the best load forecasting method in cluster microgrids, this article implements a variety of machine learning algorithms, including linear regression (quadratic), support vector machines, long short-term memory, and artificial neural networks (ANN) to forecast the load demand in the short term. The effectiveness of these methods is analyzed by computing various factors such as root mean square error, R-square, mean square error, mean absolute error, mean absolute percentage error, and time of computation. From this, it is observed that the ANN provides effective forecasting results. In addition, three distinct optimization techniques are used to find the optimum ANN training algorithm: Levenberg−Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient. The effectiveness of these optimization algorithms is verified in terms of training, test, validation, and error analysis. The proposed system simulation is carried out using the MATLAB/Simulink-2021a® software. From the results, it is found that the Levenberg−Marquardt optimization algorithm-based ANN model gives the best electrical load forecasting results.Peer reviewe
In-Network Distributed Solar Current Prediction
Long-term sensor network deployments demand careful power management. While
managing power requires understanding the amount of energy harvestable from the
local environment, current solar prediction methods rely only on recent local
history, which makes them susceptible to high variability. In this paper, we
present a model and algorithms for distributed solar current prediction, based
on multiple linear regression to predict future solar current based on local,
in-situ climatic and solar measurements. These algorithms leverage spatial
information from neighbors and adapt to the changing local conditions not
captured by global climatic information. We implement these algorithms on our
Fleck platform and run a 7-week-long experiment validating our work. In
analyzing our results from this experiment, we determined that computing our
model requires an increased energy expenditure of 4.5mJ over simpler models (on
the order of 10^{-7}% of the harvested energy) to gain a prediction improvement
of 39.7%.Comment: 28 pages, accepted at TOSN and awaiting publicatio
Short-Term Energy Demand Forecast in Hotels Using Hybrid Intelligent Modeling
This paper is the extension of the conference paper: Casteleiro-Roca, J.-L.; Gómez-González, J.F.;
Calvo-Rolle, J.L.; Jove, E.; Quintián, H.; Acosta Martín, J.F.; Gonzalez Perez, S.; Gonzalez Diaz, B.;
Calero-Garcia, F. and Méndez-Perez, J.A. Prediction of the Energy Demand of a Hotel Using an Artificial
Intelligence-Based Model. In Proceedings of the 13th International Conference, Hybrid Artificial Intelligent
Systems (HAIS), Oviedo, Spain, 20–22 June 2018.[Abstract] The hotel industry is an important energy consumer that needs efficient energy management
methods to guarantee its performance and sustainability. The new role of hotels as prosumers
increases the difficulty in the design of these methods. Also, the scenery is more complex as renewable
energy systems are present in the hotel energy mix. The performance of energy management systems
greatly depends on the use of reliable predictions for energy load. This paper presents a new
methodology to predict energy load in a hotel based on intelligent techniques. The model proposed
is based on a hybrid intelligent topology implemented with a combination of clustering techniques
and intelligent regression methods (Artificial Neural Network and Support Vector Regression).
The model includes its own energy demand information, occupancy rate, and temperature as inputs.
The validation was done using real hotel data and compared with time-series models. Forecasts
obtained were satisfactory, showing a promising potential for its use in energy management systems
in hotel resortsFundación CajaCanarias; grant number PR70575
Short-Term Load Forecasting Using AMI Data
Accurate short-term load forecasting is essential for efficient operation of
the power sector. Predicting load at a fine granularity such as individual
households or buildings is challenging due to higher volatility and uncertainty
in the load. In aggregate loads such as at grids level, the inherent
stochasticity and fluctuations are averaged-out, the problem becomes
substantially easier. We propose an approach for short-term load forecasting at
individual consumers (households) level, called Forecasting using Matrix
Factorization (FMF). FMF does not use any consumers' demographic or activity
patterns information. Therefore, it can be applied to any locality with the
readily available smart meters and weather data. We perform extensive
experiments on three benchmark datasets and demonstrate that FMF significantly
outperforms the computationally expensive state-of-the-art methods for this
problem. We achieve up to 26.5% and 24.4 % improvement in RMSE over Regression
Tree and Support Vector Machine, respectively and up to 36% and 73.2%
improvement in MAPE over Random Forest and Long Short-Term Memory neural
network, respectively
The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting
The numerous recent breakthroughs in machine learning (ML) make imperative to
carefully ponder how the scientific community can benefit from a technology
that, although not necessarily new, is today living its golden age. This Grand
Challenge review paper is focused on the present and future role of machine
learning in space weather. The purpose is twofold. On one hand, we will discuss
previous works that use ML for space weather forecasting, focusing in
particular on the few areas that have seen most activity: the forecasting of
geomagnetic indices, of relativistic electrons at geosynchronous orbits, of
solar flares occurrence, of coronal mass ejection propagation time, and of
solar wind speed. On the other hand, this paper serves as a gentle introduction
to the field of machine learning tailored to the space weather community and as
a pointer to a number of open challenges that we believe the community should
undertake in the next decade. The recurring themes throughout the review are
the need to shift our forecasting paradigm to a probabilistic approach focused
on the reliable assessment of uncertainties, and the combination of
physics-based and machine learning approaches, known as gray-box.Comment: under revie
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