End-user comfort oriented day-ahead planning for responsive residential HVAC demand aggregation considering weather forecasts

This paper extends its field of research to renovation of ordinary existing housing, the greatest characteristic of which is little personality and few styles. Distinguishing from landmark building, renovation of ordinary existing building fabric calls for highly specific approaches in planning, construction, and implementation. We, the architects develop renovation ideas and principles applied to ordinary existing housing offering our view of advocating stage renovation and knowing the real demand of the end user. Based on this view, we have chosen one typical action to discuss the efficient strategy and tactics, that cover the key areas of revitalizing community space with a focus on territoriality and sharing, improving housing function and aesthetic transformation which mainly address the integrity of facade remodeling and function upgrade, two-scale design and detail show, and adopting appropriate technology with particular emphasis on economic cost and user comfort conditions. The exploration on strategy and tactics in common renovation of ordinary existing housing aims to present references and experiences to urban residential sustainable development from both the theoretical and practical fields.

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Supervised electricity tariff prediction using random forest validated through user comfort and constraint for a home energy management scheme

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-200722 ◽

2021 ◽

Vol 40 (1) ◽

pp. 745-757

Author(s):

Ganesh Kumar Chellamani ◽

M. Firdouse Ali Khan ◽

Premanand Venkatesh Chandramani

Keyword(s):

Random Forest ◽

Energy Management ◽

Performance Metrics ◽

Support Vector ◽

End User ◽

Short Term ◽

Management Scheme ◽

Home Energy Management ◽

Electricity Tariff ◽

User Comfort

Day-ahead electricity tariff prediction is advantageous for both consumers and utilities. This article discusses the home energy management (HEM) scheme consisting of an electricity tariff predictor and appliance scheduler. The random forest (RF) technique predicts a short-term electricity tariff for the next 24 hours using the past three months of electricity tariff information. This predictor provides the tariff information to schedule the appliances at the most preferred time slot of a consumer with minimum electricity tariff, aiming high consumer comfort and low electricity bill for consumers. The proposed approach allows a user to be aware of their demand and their comfort. The proposed approach makes use of present-day (D) tariff and immediate previous 30 days (D-1, D-2, ... , D-30) of tariff information for training achieves minimum error values for next day electricity tariff prediction. The simulation results demonstrate the benefits of the RF approach for tariff prediction by comparing it with the support vector machine (SVM) and decision tree (DT) predicted tariffs against the actual tariff, provided by the utility day-ahead. The outcomes indicate that the RF produces the best results compared to SVM and DT predictions for performance metrics and end-user comfort.

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Home Energy Management System Incorporating Heat Pump Using Real Measured Data

Sensors ◽

10.3390/s19132937 ◽

2019 ◽

Vol 19 (13) ◽

pp. 2937 ◽

Cited By ~ 5

Author(s):

Cao ◽

O’Rourke ◽

Lyons ◽

Han

Keyword(s):

Energy Management ◽

Heat Pump ◽

Measured Data ◽

Management Systems ◽

End User ◽

Energy Management Systems ◽

Comfort Levels ◽

Home Energy Management ◽

The Cost ◽

User Comfort

The demand for electricity has been rising significantly over the past years and it is expected to rise further in the coming years due to economic and societal development. Smart grid technology is being developed in order to meet the rising electricity requirement. In order for the smart grid to perform its full functions, the Energy Management Systems (EMSs), especially Home Energy Management Systems (HEMS) are essential. It is necessary to understand the energy demand of the loads and the energy supply either from the national grid or from renewable energy technologies. To facilitate the Demand Side Management (DSM), Heat Pumps (HP) and air conditioning systems are often utilised for heating and cooling in residential houses due to their high-efficiency power output and low CO2 emissions. This paper presents a program for a HEMS using a Particle Swarm Optimisation (PSO) algorithm. A HP is used as the load and the aim of the optimisation program is to minimise the operational cost, i.e., the cost of electricity, while maintaining end-user comfort levels. This paper also details an indoor thermal model for temperature update in the heat pump control program. Real measured data from the UK Government’s Renewable Heat Premium Payment (RHPP) scheme was utilised to generate characteristic curves and equations that can represent the data. This paper compares different PSO variants with standard PSO and the unscheduled case calculated from the data for five winter days in 2019. Among all chosen algorithms, the Crossover Subswarm PSO (CSPSO) achieved an average saving of 25.61% compared with the cost calculated from the measured data with a short search time of 1576 ms for each subswarm. It is clear from this work that there is significant scope to reduce the cost of operating a HP while maintaining end user comfort levels.

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Investigation of direct load management scheme with consideration of end-user comfort

Electrical Engineering in Japan ◽

10.1002/eej.21139 ◽

2011 ◽

Vol 176 (3) ◽

pp. 19-28

Author(s):

Junji Kondoh ◽

Ahmed Shafiu ◽

Nick Jenkins ◽

Danny Pudjianto ◽

Goran Strbac

Keyword(s):

Load Management ◽

End User ◽

Management Scheme ◽

User Comfort

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A multiyear intercomparison of wet troposphere corrections from TOPEX/Poseidon, ERS-1, and ERS-2 Microwave Radiometers and the European Center for Medium-Range Weather Forecasts model

Journal of Geophysical Research Atmospheres ◽

10.1029/2000jc000486 ◽

2001 ◽

Vol 106 (C9) ◽

pp. 19657-19670 ◽

Cited By ~ 1

Author(s):

T. J. Urban

Keyword(s):

Weather Forecasts ◽

Medium Range ◽

Microwave Radiometers

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Reducing Probabilistic Weather Forecasts to the Worst Case Scenario: Anchoring Effects

PsycEXTRA Dataset ◽

10.1037/e527312012-643 ◽

2008 ◽

Author(s):

Sonia Savelli ◽

Susan Joslyn ◽

Limor Nadav-Greenberg ◽

Queena Chen

Keyword(s):

Case Scenario ◽

Worst Case ◽

Weather Forecasts ◽

Worst Case Scenario ◽

Anchoring Effects

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Technology characteristics predicting end user acceptance of smart intravenous infusion pumps

PsycEXTRA Dataset ◽

10.1037/e577352012-019 ◽

2005 ◽

Cited By ~ 1

Author(s):

Tosha B. Wetterneck ◽

Pascale Carayon ◽

Folasade Sobande ◽

Ann Schoofs Hundt

Keyword(s):

Intravenous Infusion ◽

User Acceptance ◽

End User ◽

Infusion Pumps

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Analisis Tingkat Kepuasan Pengguna Aplikasi UG In Your Hand Dengan Metode End User Computing Satisfaction (EUCS)

ICIT Journal ◽

10.33050/icit.v6i2.1118 ◽

2020 ◽

Vol 6 (2) ◽

pp. 213-226

Author(s):

Eka Fitri Rahayu

Keyword(s):

Ease Of Use ◽

End User ◽

End User Computing

Informasi sangat dibutuhkan dalam pengambilan keputusan. Sistem informasi yang efektif adalah sistem informasi yang harus mampu memberikan kepuasan kepada para penggunanya. Tujuan dari penelitian ini adalah untuk mengetahui pengaruh model End User Computing Satisfaction (EUCS) dengan Instrumen Accuracy, Content, Format, Timeliness dan Ease of Use terhadap kepuasan pengguna sistem UG In Your Hand. Data yang digunakan sebanyak 96 responden yang diperoleh melalui kuesioner online yang dibagikan kepada Mahasiswa. Metode penelitian menggunakan model Regresi Linier Berganda. Hasil penelitian menunjukkan bahwa Instrumen Ease of Use dan Timeliness mempengaruhi kepuasan pengguna sistem, selain itu Instrumen Content, Accuracy dan Format tidak mempengaruhi kepuasan pengguna sistem UG In Your Hand. Kata Kunci : Kepuasan Pengguna, UG In Your Hand, Metode EUCS

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