scholarly journals Self-Learning Algorithm to Predict Indoor Temperature and Cooling Demand from Smart WiFi Thermostat in a Residential Building

2020 ◽  
Vol 12 (17) ◽  
pp. 7110
Author(s):  
Kefan Huang ◽  
Kevin P. Hallinan ◽  
Robert Lou ◽  
Abdulrahman Alanezi ◽  
Salahaldin Alshatshati ◽  
...  
Keyword(s):  
Thermal Comfort ◽  
Energy Savings ◽  
Dynamic Models ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Peak Load ◽  
Back Propagation ◽  
Residential Building ◽  
Measured Temperature ◽  
Heating And Cooling

Smart WiFi thermostats have moved well beyond the function they were originally designed for; namely, controlling heating and cooling comfort in buildings. They are now also learning from occupant behaviors and permit occupants to control their comfort remotely. This research seeks to go beyond this state of the art by utilizing smart WiFi thermostat data in residences to develop dynamic predictive models for room temperature and cooling/heating demand. These models can then be used to estimate the energy savings from new thermostat temperature schedules and estimate peak load reduction achievable from maintaining a residence in a minimum thermal comfort condition. Back Propagation Neural Network (BPNN), Long-Short Term Memory (LSTM), and Encoder-Decoder LSTM dynamic models are explored. Results demonstrate that LSTM outperforms BPNN and Encoder-Decoder LSTM approach, yielding and a MAE error of 0.5 °C, equal to the resolution error of the measured temperature. Additionally, the models developed are shown to be highly accurate in predicting savings from aggressive thermostat set point schedules, yielding deep reduction of up to 14.3% for heating and cooling, as well as significant energy reduction from curtailed thermal comfort in response to a high demand event.

scholarly journals Towards Sustainable Energy Retrofitting, a Simulation for Potential Energy Use Reduction in Residential Buildings in Palestine

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3876
Author(s):  
Sameh Monna ◽  
Adel Juaidi ◽  
Ramez Abdallah ◽  
Aiman Albatayneh ◽  
Patrick Dutournie ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Energy Use ◽  
Energy Savings ◽  
Residential Buildings ◽  
Residential Building ◽  
Simulation Models ◽  
Water Heating ◽  
Heating And Cooling ◽  
Space Cooling ◽  
Cooling Loads

Since buildings are one of the major contributors to global warming, efforts should be intensified to make them more energy-efficient, particularly existing buildings. This research intends to analyze the energy savings from a suggested retrofitting program using energy simulation for typical existing residential buildings. For the assessment of the energy retrofitting program using computer simulation, the most commonly utilized residential building types were selected. The energy consumption of those selected residential buildings was assessed, and a baseline for evaluating energy retrofitting was established. Three levels of retrofitting programs were implemented. These levels were ordered by cost, with the first level being the least costly and the third level is the most expensive. The simulation models were created for two different types of buildings in three different climatic zones in Palestine. The findings suggest that water heating, space heating, space cooling, and electric lighting are the highest energy consumers in ordinary houses. Level one measures resulted in a 19–24 percent decrease in energy consumption due to reduced heating and cooling loads. The use of a combination of levels one and two resulted in a decrease of energy consumption for heating, cooling, and lighting by 50–57%. The use of the three levels resulted in a decrease of 71–80% in total energy usage for heating, cooling, lighting, water heating, and air conditioning.

scholarly journals Thermochromic Paints on External Surfaces: Impact Assessment for a Residential Building through Thermal and Energy Simulation

Energies ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 1912 ◽  
Author(s):  
Vasco Granadeiro ◽  
Margarida Almeida ◽  
Tiago Souto ◽  
Vítor Leal ◽  
João Machado ◽  
...  
Keyword(s):  
Energy Use ◽  
Energy Savings ◽  
Residential Buildings ◽  
Residential Building ◽  
Cost Effective ◽  
Physical Models ◽  
Indoor Temperature ◽  
Heating And Cooling ◽  
Step Transition ◽  
Linear Manner

This work addresses the effect of using thermochromic paints in residential buildings. Two different thermochromic paint types were considered: One that changes properties through a step transition at a certain temperature, and another that changes properties in a gradual/linear manner throughout a temperature range. The studied building was a two-floor villa, virtually simulated through a digital model with and without thermal insulation, and considering thermochromic paints applied both on external walls and on the roof. The performance assessment was done through the energy use for heating and cooling (in conditioned mode), as well as in terms of the indoor temperature (in free-floating mode). Three different cities/climates were considered: Porto, Madrid, and Abu Dhabi. Results showed that energy savings up to 50.6% could be reached if the building is operated in conditioned mode. Conversely, when operated in free-floating mode, optimally selected thermochromic paints enable reductions up to 11.0 °C, during summertime, and an increase up to 2.7 °C, during wintertime. These results point out the great benefits of using optimally selected thermochromic paints for obtaining thermal comfort, and also the need to further develop stable and cost-effective thermochromic pigments for outdoor applications, as well as to test physical models in a real environment.

Improvement of Variant Adaptable LSTM Trained With Metaheuristic Algorithms for Healthcare Analysis

2021 ◽  
pp. 1031-1051
Author(s):  
Tarik A. Rashid ◽  
Mohammad K. Hassan ◽  
Mokhtar Mohammadi ◽  
Kym Fraser
Keyword(s):  
Diabetes Mellitus ◽  
Neural Networks ◽  
Time Series Data ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Machine Learning Techniques ◽  
Series Data ◽  
Data Sets ◽  
Patients With Diabetes

Recently, the population of the world has increased along with health problems. Diabetes mellitus disease as an example causes issues to the health of many patients globally. The task of this chapter is to develop a dynamic and intelligent decision support system for patients with different diseases, and it aims at examining machine-learning techniques supported by optimization techniques. Artificial neural networks have been used in healthcare for several decades. Most research works utilize multilayer layer perceptron (MLP) trained with back propagation (BP) learning algorithm to achieve diabetes mellitus classification. Nonetheless, MLP has some drawbacks, such as, convergence, which can be slow; local minima can affect the training process. It is hard to scale and cannot be used with time series data sets. To overcome these drawbacks, long short-term memory (LSTM) is suggested, which is a more advanced form of recurrent neural networks. In this chapter, adaptable LSTM trained with two optimizing algorithms instead of the back propagation learning algorithm is presented. The optimization algorithms are biogeography-based optimization (BBO) and genetic algorithm (GA). Dataset is collected locally and another benchmark dataset is used as well. Finally, the datasets fed into adaptable models; LSTM with BBO (LSTMBBO) and LSTM with GA (LSTMGA) for classification purposes. The experimental and testing results are compared and they are promising. This system helps physicians and doctors to provide proper health treatment for patients with diabetes mellitus. Details of source code and implementation of our system can be obtained in the following link “https://github.com/hamakamal/LSTM.”

scholarly journals The use of green walls and the impact on air quality and life standard

2019 ◽  
Vol 116 ◽  
pp. 00096
Author(s):  
Małgorzata Wesołowska ◽  
Marta Laska
Keyword(s):  
Urban Areas ◽  
Energy Savings ◽  
Traffic Noise ◽  
Residential Building ◽  
High Energy ◽  
Large Cities ◽  
Heating And Cooling ◽  
Air Pollutions ◽  
Green Walls ◽  
The Impact

People living in urban areas are exposed to a number of threats related with dense urban tissue and high number of vehicles. These include air pollutions, traffic noise and high temperatures. In addition, large cities are struggling with high energy consumption for heating and cooling purposes. One of the possibilities to reduce the mentioned undesirable effects is the use of vegetation on the walls. Plants absorbs the pollutants of air, produced the oxygen, mounted on external walls create thermal insulation and positively affect the psychological aspect. Green walls can be used both indoors and outdoors. The article presents literature review on green walls, describes their benefits and presents the calculations SPBT and possible energy savings taking into account the transmission losses for small residential building.

scholarly journals Estimating Smart Wi-Fi Thermostat-Enabled Thermal Comfort Control Savings for Any Residence

2021 ◽  
Vol 3 (4) ◽  
pp. 743-760
Author(s):  
Abdulelah D. Alhamayani ◽  
Qiancheng Sun ◽  
Kevin P. Hallinan
Keyword(s):  
Thermal Comfort ◽  
Energy Savings ◽  
Short Term Memory ◽  
Control Strategies ◽  
Residential Buildings ◽  
Real Data ◽  
Accurate Estimation ◽  
Indoor Temperature ◽  
Vote Model ◽  
Neural Network Algorithm

Nowadays, most indoor cooling control strategies are based solely on the dry-bulb temperature, which is not close to a guarantee of thermal comfort of occupants. Prior research has shown cooling energy savings from use of a thermal comfort control methodology ranging from 10 to 85%. The present research advances prior research to enable thermal comfort control in residential buildings using a smart Wi-Fi thermostat. “Fanger’s Predicted Mean Vote model” is used to define thermal comfort. A machine learning model leveraging historical smart Wi-Fi thermostat data and outdoor temperature is trained to predict indoor temperature. A Long Short-Term-Memory neural network algorithm is employed for this purpose. The model considers solar heat input estimations to a residence as input features. The results show that this approach yields a substantially improved ability to accurately model and predict indoor temperature. Secondly, it enables a more accurate estimation of potential savings from thermal comfort control. Cooling energy savings ranging from 33 to 47% are estimated based upon real data for variable energy effectiveness and solar exposed residences.

scholarly journals Development of strategy for combined smart ventilated window and PCM energy storage control for residential building energy saving

2021 ◽  
Vol 2042 (1) ◽  
pp. 012161
Author(s):  
Yue Hu ◽  
Per Kvols Heiselberg
Keyword(s):  
Energy Storage ◽  
Thermal Comfort ◽  
Energy Saving ◽  
Control Strategy ◽  
Energy Savings ◽  
Control Strategies ◽  
Average Energy ◽  
Residential Building ◽  
Energy Performance ◽  
Before And After

Abstract The paper studies the energy renovation of a residential building with new façade solutions combining smart ventilated window (VW) and PCM energy storage and the corresponding control strategy to ensure energy savings. The study is carried out by Energyplus modelling comparing the energy consumption and thermal comfort of an apartment before and after renovation. A detailed control strategy is introduced and simulated. The modelling results of the apartment before and after retrofit indicate that with the designed control strategies, the average energy saving percentage of the apartment with PCM energy storage and VW compared to the apartment without PCM energy storage and VW is 29%. The rooms with PCMVWs achieve higher energy saving than the rooms with only VWs. The PCM energy storage improves energy performance of the VWs for both heating and cooling seasons. With the renovation, the thermal comfort of all the rooms are improved for cooling season.

ANALYSIS FOR THERMAL BEHAVIOR AND ENERGY SAVINGS OF A SEMI-DETACHED HOUSE WITH DIFFERENT INSULATION STRATEGIES IN A HOT SEMI-ARID CLIMATE

2017 ◽  
Vol 12 (1) ◽  
pp. 78-106 ◽  
Author(s):  
Issam Sobhy ◽  
Abderrahim Brakez ◽  
Brahim Benhamou
Keyword(s):  
Thermal Insulation ◽  
Energy Savings ◽  
Thermal Inertia ◽  
Residential Building ◽  
Cavity Wall ◽  
Arid Climate ◽  
Heating And Cooling ◽  
Detached House ◽  
Cooling Loads ◽  
Semi Arid

The purpose of this research is to assess thermal performance and energy saving of a residential building in the hot semi-arid climate of Marrakech (Morocco). The studied house is built as usual in Marrakech without any thermal insulation except for its external walls, facing East and West, which are double walls with a 5 cm air gap in between (“cavity wall” technique). The cavity wall effective thermal conductivity was carefully calculated taking into account both radiation and convection heat transfers. Experimental results, obtained from winter and summer monitoring of the house, show well dampening of air temperature, thanks to its thermal inertia. However, this temperature remained outside the standard thermal comfort zone leading to large cooling/heating load. Simulation results indicate that the cavity wall contributes to an overall reduction of 13% and 5% of the house heating and cooling loads respectively. Moreover, the addition of XPS roof thermal insulation significantly enhances the heating and cooling energy savings to 26% and 40% respectively.

scholarly journals Tuning radiative heat flows between interior surfaces and human occupants to improve heating and cooling efficiency

2020 ◽  
Author(s):  
Jin Xu ◽  
Aaswath Raman
Keyword(s):  
Thermal Comfort ◽  
Energy Use ◽  
Energy Savings ◽  
Weather Conditions ◽  
Cold Weather ◽  
Set Point ◽  
Heating And Cooling ◽  
Building Surfaces ◽  
Interior Walls ◽  
High Emissivity

Space heating and cooling in buildings account for nearly 20% of energy use globally. In most buildings this energy is used to maintain the thermal comfort of the building’s human occupants by maintaining the interior air temperature at a particular set point. However, if one could maintain the human occupant’s thermal comfort while decreasing the heating or increasing the cooling set point, dramatic energy savings are possible. Here, we propose and evaluate an untapped degree of freedom in improving building efficiency: dynamically tuning the thermal emissivity of interior building surfaces at long-wave infrared wavelengths to maintain thermal comfort. We show that in cold weather conditions tuning the emissivity of interior walls, floors and ceilings to a low value (0.1) can decrease the set point temperature as much as 7°C, corresponding to an energy saving of nearly 67.7% relative to high emissivity materials (0.9). Conversely, in warm weather, high emissivity interior surfaces result in a 38.5% energy savings relative to low emissivity surfaces, highlighting the need for tunability for maximal year-round efficiency. Our results reveal the remarkable energy savings potential possible by better controlling the ubiquitous flows of heat that surround us in the form of thermal radiation.

scholarly journals CHANGES IN LIGHTING STANDARDS AND THEIR INFLUENCE ON THE ARCHITECTURE AND ENERGY EFFICIENCY OF MODERN RESIDENTIAL BUILDINGS

2020 ◽  
pp. 9-17
Author(s):  
E.V. Vitvitskaya ◽  
◽  
D.V. Tarasevich ◽  
Keyword(s):  
Heat Transfer ◽  
Energy Efficiency ◽  
Energy Savings ◽  
Residential Buildings ◽  
Residential Building ◽  
High Heat ◽  
Transfer Resistance ◽  
Heating And Cooling ◽  
Shielding Properties ◽  
Heat Shielding

Abstract. State regulations on the design of lighting in residential buildings in recent years have undergone significant changes, which in turn will significantly affect the architecture and energy efficiency of modern buildings of this type. This can be observed from the authors' analysis of the change in only one regulatory document given in this article – SCS (State Construction Standards) V.2.5-28: «Natural and artificial lighting» and only one lighting indicator: permissible deviation of the calculated value of CNL (coefficient of natural lighting) from the standardized value when choosing translucent structures of buildings. This article presents an analysis of this normative document in two versions – in the old one from 2012 and new from 2018. Based on the results of the analysis, the authors of this article found that, at the request of the architect, the area of translucent structures on the facades of two identical modern residential buildings can differ significantly: from the minimum with piece (separate) windows on the facades – where glazing occupies from 14.3% to 18.3% of the area of the facades; up to maximum with continuous glazing of facades – where glazing occupies up to 100% of the area of the facades of a residential building. These two facade glazing options are not only architecturally perceived differently, but they must also have different energy efficiency in order to provide different minimum allowable values of heat transfer resistance: for piece (individual) windows on the facade, this is R∑ ≥ Rq min = 0.6 m2•K/W and ordinary silicate glasses are suitable for their glazing, and for continuous glazing of the facade this should already be R∑ ≥ Rq min = 2.8 m2•K/W, that is, they must have the same heat-shielding properties as the outer walls, and their minimum allowable value of the heat transfer resistance must be 4.66 times more than for piece (separate) windows. For this option, ordinary silicate glass is no longer suitable, but modern glass-transparent structures with high heat-shielding properties should be used, for example Qbiss_Air, Pilkington, Heat Mirror Glass and others. They provide excellent protection against hypothermia in winter and overheating in summer, and have good sun protection properties. Their use in modern buildings contributes to energy savings for heating and cooling rooms throughout the year and creates increased comfort, but such translucent structures are much more expensive and better suited for elite housing construction than for social.

scholarly journals Tuning radiative heat flows between interior surfaces and human occupants to improve heating and cooling efficiency

2020 ◽  
Author(s):  
Aaswath Raman ◽  
Jin Xu
Keyword(s):  
Thermal Comfort ◽  
Energy Use ◽  
Energy Savings ◽  
Weather Conditions ◽  
Cold Weather ◽  
Set Point ◽  
Heating And Cooling ◽  
Building Surfaces ◽  
Interior Walls ◽  
High Emissivity

Space heating and cooling in buildings account for nearly 20% of energy use globally. In most buildings this energy is used to maintain the thermal comfort of the building’s human occupants by maintaining the interior air temperature at a particular set point. However, if one could maintain the human occupant’s thermal comfort while decreasing the heating or increasing the cooling set point, dramatic energy savings are possible. Here, we propose and evaluate an untapped degree of freedom in improving building efficiency: dynamically tuning the thermal emissivity of interior building surfaces at long-wave infrared wavelengths to maintain thermal comfort. We show that in cold weather conditions tuning the emissivity of interior walls, floors and ceilings to a low value (0.1) can decrease the set point temperature as much as 7°C, corresponding to an energy saving of nearly 67.7% relative to high emissivity materials (0.9). Conversely, in warm weather, high emissivity interior surfaces result in a 38.5% energy savings relative to low emissivity surfaces, highlighting the need for tunability for maximal year-round efficiency. Our results reveal the remarkable energy savings potential possible by better controlling the ubiquitous flows of heat that surround us in the form of thermal radiation.

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