Smart Electric Energy in Buildings

Abstract submission deadline

15 May 2025

Manuscript submission deadline

15 July 2025

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12672

Topic Information

Dear Colleagues,

The development of technologies based on sensors, data, communications, and computation can help to increase energy efficiency and to have more sustainable buildings. In particular, in the case of electric energy, there is a long way to go in terms of integrating renewable energy, energy storage, energy sharing, or reducing consumption. In this Topic, we invite submissions of research papers that deal with at least one of the following aspects, considering houses, buildings, condominiums, or any other group of living places:

-The application of enabling technologies to the electric energy in a house or in a building, i.e., Big Data, Artificial Intelligence, Digital Twin, Internet of Things, etc…;

-Technologies, scenarios, and methodologies in storing electric energy;

-Development of renewable and alternative sources of energy;

-Changes in distribution, routines, equipment that can help to have healthier, and more comfortable living places;

-Different approaches to energy management, depending on the scenario;

-Optimization of energy storage, consumption, charge of Electric Vehicles, etc…;

-Upgrades of electric service, in terms of continuity, flexibility, availability, etc…;

-Integration of buildings with Smart Cities;

-Analysis of trends and challenges to solve in the electrical and/or renewable energy installation.

Prof. Dr. Daniel Villanueva Torres
Dr. Ali Hainoun
Prof. Dr. Sergio Gómez Melgar
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.7	4.5	2011	16.9 Days	CHF 2400	Submit
Energies energies	3.2	5.5	2008	16.1 Days	CHF 2600	Submit
Buildings buildings	3.8	3.1	2011	14.6 Days	CHF 2600	Submit
Smart Cities smartcities	6.4	8.5	2018	20.2 Days	CHF 2000	Submit
AI ai	-	-	2020	20.8 Days	CHF 1600	Submit

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Published Papers (6 papers)

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All Applied Sciences Energies Buildings Smart Cities AI

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20 pages, 2178 KiB  

Open AccessArticle

A Distributed Intelligent Lighting Control System Based on Deep Reinforcement Learning

by Peixin Fang, Ming Wang, Jingzheng Li, Qianchuan Zhao, Xuehan Zheng and He Gao

Appl. Sci. 2023, 13(16), 9057; https://doi.org/10.3390/app13169057 - 08 Aug 2023

Cited by 1 | Viewed by 1168

Abstract

With the rapid development of human society, people’s requirements for lighting are also increasing. The amount of energy consumed by lighting systems in buildings is increasing, but most current lighting systems are inefficient and provide insufficient light comfort. Therefore, this paper proposes an [...] Read more.

With the rapid development of human society, people’s requirements for lighting are also increasing. The amount of energy consumed by lighting systems in buildings is increasing, but most current lighting systems are inefficient and provide insufficient light comfort. Therefore, this paper proposes an intelligent lighting control system based on a distributed architecture, incorporating a dynamic shading system for adjusting the interior lighting environment. The system comprises two subsystems: lighting and shading. The shading subsystem utilizes fuzzy control logic to control lighting based on the room’s temperature and illumination, thereby achieving rapid control with fewer calculations. The lighting subsystem employs a Deep Deterministic Policy Gradient (DDPG) algorithm to optimize the luminaire dimming problem based on room illuminance in order to maximize user convenience while achieving uniform illumination. This paper also includes the construction of a prototype box on which the system is evaluated in two distinct circumstances. The results of the tests demonstrate that the system functions properly, has stability and real-time performance, and can adapt to complex and variable outdoor environments. The maximum relative error between actual and expected illuminance is less than 10%, and the average relative error is less than 5% when achieving uniform illuminance. Full article

(This article belongs to the Topic Smart Electric Energy in Buildings)

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17 pages, 3851 KiB  

Open AccessArticle

FastInformer-HEMS: A Lightweight Optimization Algorithm for Home Energy Management Systems

by Xihui Chen and Dejun Ning

Energies 2023, 16(9), 3897; https://doi.org/10.3390/en16093897 - 05 May 2023

Cited by 1 | Viewed by 1657

Abstract

In a smart home with distributed energy resources, the home energy management system (HEMS) controls the photovoltaic (PV) storage system by executing the optimization algorithm to achieve the lowest power cost. The existing mixed integer linear programming (MILP) algorithm is not suitable for [...] Read more.

In a smart home with distributed energy resources, the home energy management system (HEMS) controls the photovoltaic (PV) storage system by executing the optimization algorithm to achieve the lowest power cost. The existing mixed integer linear programming (MILP) algorithm is not suitable for execution on the end-user side due to its high computational complexity. The HEMS algorithm based on a long short-term memory neural network (LSTM-HEMS) can effectively solve the problem of the high computational complexity of MILP, but its optimization outcome is not high due to the accumulation of prediction errors. In order to achieve a better balance between computational complexity and optimization outcome, this paper proposes a lightweight optimization algorithm called the FastInformer-HEMS, which introduces the E-Attn attention mechanism based on Informer and uses global average pooling to extract the attention characteristics. Meanwhile, the proposed method introduces the maximum self-consumption algorithm as a backup strategy to ensure the safe operation of the system. The simulated results show that the computational complexity of the proposed FastInformer-HEMS is the lowest among the existing algorithms. Compared with the existing LSTM-HEMS, the proposed algorithm reduces the power consumption cost by 12.3% and 6.6% in the two typical scenarios, while the execution time decreases by 13.6 times. Full article

(This article belongs to the Topic Smart Electric Energy in Buildings)

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15 pages, 2094 KiB  

Open AccessArticle

Non-Intrusive Load Decomposition Based on Instance-Batch Normalization Networks

by Mao Wang, Dandan Liu and Changzhi Li

Energies 2023, 16(7), 2940; https://doi.org/10.3390/en16072940 - 23 Mar 2023

Viewed by 910

Abstract

At present, the non-intrusive load decomposition method for low-frequency sampling data is as yet insufficient within the context of generalization performance, failing to meet the decomposition accuracy requirements when applied to novel scenarios. To address this issue, a non-intrusive load decomposition method based [...] Read more.

At present, the non-intrusive load decomposition method for low-frequency sampling data is as yet insufficient within the context of generalization performance, failing to meet the decomposition accuracy requirements when applied to novel scenarios. To address this issue, a non-intrusive load decomposition method based on instance-batch normalization network is proposed. This method uses an encoder-decoder structure with attention mechanism, in which skip connections are introduced at the corresponding layers of the encoder and decoder. In this way, the decoder can reconstruct a more accurate power sequence of the target. The proposed model was tested on two public datasets, REDD and UKDALE, and the performance was compared with mainstream algorithms. The results show that the F1 score was higher by an average of 18.4 when compared with mainstream algorithms. Additionally, the mean absolute error reduced by an average of 25%, and the root mean square error was reduced by an average of 22%. Full article

(This article belongs to the Topic Smart Electric Energy in Buildings)

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26 pages, 35113 KiB  

Open AccessArticle

Smarter Together: Monitoring and Evaluation of Integrated Building Solutions for Low-Energy Districts of Lighthouse Cities Lyon, Munich, and Vienna

by Ali Hainoun, Hans-Martin Neumann, Naomi Morishita-Steffen, Baptiste Mougeot, Étienne Vignali, Florian Mandel, Felix Hörmann, Sebastian Stortecky, Katharina Walter, Martin Kaltenhauser-Barth, Bojan Schnabl, Stephan Hartmann, Maxime Valentin, Bruno Gaiddon, Samuel Martin and Benoit Rozel

Energies 2022, 15(19), 6907; https://doi.org/10.3390/en15196907 - 21 Sep 2022

Cited by 3 | Viewed by 2202

Abstract

The Smarter Together project implemented in the three lighthouse cities (LHCs) of Lyon, Munich, and Vienna a set of co-created and integrated smart solutions for a better life in urban districts. The implemented solutions have been monitored using a novel integrated monitoring methodology [...] Read more.

The Smarter Together project implemented in the three lighthouse cities (LHCs) of Lyon, Munich, and Vienna a set of co-created and integrated smart solutions for a better life in urban districts. The implemented solutions have been monitored using a novel integrated monitoring methodology (IMM) following a co-creation process involving key stakeholders of the LHCs. With focus on holistic building refurbishment and the integration of onsite renewable energy supply (RES), the three LHCs refurbished around 117,497 m² of floor area and constructed 12,446 m² of new floor area. They implemented around 833 kWp of PV, 35 kW of solar thermal and 13,122 kW of geothermal heating systems. Altogether, the realized solutions for low-energy districts in the three LHCs will annually save around 4000 MWh/a, generate 1145 MWh/a of RES and reduce around 1496 tCO₂/a of CO₂ emissions, corresponding to specific values of 37.6 kWh/m².a and 11.9 kg-CO₂/m².a for final energy saving and CO₂ emission reductions, respectively. KPI-based monitoring and evaluation of the implemented solutions provides qualitative and quantitative insight, experience and lessons learned to optimize the process of implementation and deployment of integrated solutions for holistic building refurbishment, and thus contribute to advancing sustainable urban transformation at the district level for both LHCs and FCs. Full article

(This article belongs to the Topic Smart Electric Energy in Buildings)

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16 pages, 3546 KiB  

Open AccessArticle

Short-Term Load Forecasting on Individual Consumers

by João Victor Jales Melo, George Rossany Soares Lira, Edson Guedes Costa, Antonio F. Leite Neto and Iago B. Oliveira

Energies 2022, 15(16), 5856; https://doi.org/10.3390/en15165856 - 12 Aug 2022

Viewed by 1528

Abstract

Maintaining stability and control over the electric system requires increasing information about the consumers’ profiling due to changes in the form of electricity generation and consumption. To overcome this trouble, short-term load forecasting (STLF) on individual consumers gained importance in the last years. [...] Read more.

Maintaining stability and control over the electric system requires increasing information about the consumers’ profiling due to changes in the form of electricity generation and consumption. To overcome this trouble, short-term load forecasting (STLF) on individual consumers gained importance in the last years. Nonetheless, predicting the profile of an individual consumer is a difficult task. The main challenge lies in the uncertainty related to the individual consumption profile, which increases forecasting errors. Thus, this paper aims to implement a load predictive model focused on individual consumers taking into account its randomness. For this purpose, a methodology is proposed to determine and select predictive features for individual STLF. The load forecasting of an individual consumer is simulated based on the four main machine learning techniques used in the literature. A 2.73% reduction in the forecast error is obtained after the correct selection of the predictive features. Compared to the baseline model (persistent forecasting method), the error is reduced by up to 19.8%. Among the techniques analyzed, support vector regression (SVR) showed the smallest errors (8.88% and 9.31%). Full article

(This article belongs to the Topic Smart Electric Energy in Buildings)

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18 pages, 12271 KiB  

Open AccessFeature PaperArticle

Modeling and Simulation of Household Appliances Power Consumption

by Daniel Villanueva, Diego San-Facundo, Edelmiro Miguez-García and Antonio Fernández-Otero

Appl. Sci. 2022, 12(7), 3689; https://doi.org/10.3390/app12073689 - 06 Apr 2022

Cited by 6 | Viewed by 3150

Abstract

The consumption of household appliances tends to increase. Therefore, the application of energy efficiency measurements is urgently needed to reduce the levels of power consumption. Over the last years, various methods have been used to predict household electricity consumption. As a novelty, this [...] Read more.

The consumption of household appliances tends to increase. Therefore, the application of energy efficiency measurements is urgently needed to reduce the levels of power consumption. Over the last years, various methods have been used to predict household electricity consumption. As a novelty, this paper proposed a method of predicting the consumption of household appliances by evaluating statistical distributions (Kolmogorov–Smirnov Test and Pearson’s X² test). To test the veracity of the evaluations, first, a set of random values was simulated for each hour, and their respective averages were calculated. These were compared with the averages of the real values for each hour. With the exception of HVAC during working days, great results were obtained. For the refrigerator, the maximum error was 3.91%, while for the lighting, it was 4.27%. At the point of consumption, the accuracy was even higher, with an error of 1.17% for the dryer while for the washing machine and dishwasher, their minimum errors were less than 1%. The error results confirm that the applied methodology is perfectly acceptable for modeling household appliance consumption and consequently predicting it. However, these consumptions can be only extrapolated to dwellings with similar surface areas and habitats. Full article

(This article belongs to the Topic Smart Electric Energy in Buildings)

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