Papers

@article{cedron_efficient_2024,

title = {Efficient Implementation of Multilayer Perceptrons: Reducing Execution Time and Memory Consumption},

author = {Francisco Cedrón and Sara Álvarez-González and Ana Ribas-Rodríguez and Santiago Rodríguez-Yáñez and Ana Belén Porto-Pazos},

url = {https://www.mdpi.com/2076-3417/14/17/8020},

doi = {10.3390/app14178020},

issn = {2076-3417},

year  = {2024},

date = {2024-01-01},

urldate = {2026-04-28},

journal = {Applied Sciences},

volume = {14},

number = {17},

pages = {8020},

publisher = {Multidisciplinary Digital Publishing Institute},

abstract = {A technique is presented that reduces the required memory of neural networks through improving weight storage. In contrast to traditional methods, which have an exponential memory overhead with the increase in network size, the proposed method stores only the number of connections between neurons. The proposed method is evaluated on feedforward networks and demonstrates memory saving capabilities of up to almost 80% while also being more efficient, especially with larger architectures.},

keywords = {compressed weight matrix, multilayer perceptron, Neural networks, sparsity, weight density},

pubstate = {published},

tppubtype = {article}

}

@article{bienvenido-huertas_estimating_2019,

title = {Estimating Adaptive Setpoint Temperatures Using Weather Stations},

author = {David Bienvenido-Huertas and Carlos Rubio-Bellido and Juan Luis Pérez-Ordóñez and Fernando Martínez-Abella},

url = {https://www.mdpi.com/1996-1073/12/7/1197},

doi = {10.3390/en12071197},

issn = {1996-1073},

year  = {2019},

date = {2019-01-01},

urldate = {2026-08-06},

journal = {Energies},

volume = {12},

number = {7},

pages = {1197},

publisher = {Multidisciplinary Digital Publishing Institute},

abstract = {Reducing both the energy consumption and CO2 emissions of buildings is nowadays one of the main objectives of society. The use of heating and cooling equipment is among the main causes of energy consumption. Therefore, reducing their consumption guarantees such a goal. In this context, the use of adaptive setpoint temperatures allows such energy consumption to be significantly decreased. However, having reliable data from an external temperature probe is not always possible due to various factors. This research studies the estimation of such temperatures without using external temperature probes. For this purpose, a methodology which consists of collecting data from 10 weather stations of Galicia is carried out, and prediction models (multivariable linear regression (MLR) and multilayer perceptron (MLP)) are applied based on two approaches: (1) using both the setpoint temperature and the mean daily external temperature from the previous day; and (2) using the mean daily external temperature from the previous 7 days. Both prediction models provide adequate performances for approach 1, obtaining accurate results between 1 month (MLR) and 5 months (MLP). However, for approach 2, only the MLP obtained accurate results from the 6th month. This research ensures the continuity of using adaptive setpoint temperatures even in case of possible measurement errors or failures of the external temperature probes.},

keywords = {adaptive setpoint temperature, multilayer perceptron, multivariable linear regression, weather station},

pubstate = {published},

tppubtype = {article}

}