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Temperature Forecasts with Stable Accuracy in a Smart Home

Bruce Spencer, Feras Al-Obeidat
2016 Procedia Computer Science  
We forecast internal temperature in a home with sensors, modeled as a linear function of recent sensor values.  ...  When delivering forecasts as a service, two desirable properties are that forecasts have stable accuracy over a variety of forecast horizons -so service levels can be predicted -and that the forecasts  ...  In this paper we consider forecast accuracy in a domotic or "smart" house, containing sensors.  ... 
doi:10.1016/j.procs.2016.04.160 fatcat:5futaieyjfcmbakm2x3cksdryq

Short Term Forecasts of Internal Temperature with Stable Accuracy in Smart Homes

2017 International Journal of Thermal and Environmental Engineering  
We forecast internal temperature in two homes, using variants of regression with data from the readings of multiple sensors.  ...  We use 48 separate models, where each forecasts mean temperatures that will occur in one future 15-minute interval, to compose a forecast for the next 12 hours.  ...  Our RMSE over the 12 hours is 0.09 with a maximal RMSE of any one forecast at 0.10 indicating our accuracy is stable for forecasting temperature differences.  ... 
doi:10.5383/ijtee.13.02.002 fatcat:angmhq5jzrdzrowb4zv6s2ww7u

Short Term Electricity Forecasting Using Individual Smart Meter Data

Krzysztof Gajowniczek, Tomasz Ząbkowski
2014 Procedia Computer Science  
Smart metering is a quite new topic that has grown in importance all over the world and it appears to be a remedy for rising prices of electricity.  ...  Forecasting electricity usage is an important task to provide intelligence to the smart gird.  ...  Acknowledgements This research was financed by VEDIA S.A. leading a project partially supported by National Centre for Research and Development in Poland (NCBiR).  ... 
doi:10.1016/j.procs.2014.08.140 fatcat:grzyqcf5vbhfzoup6ek5aqxxmm

Machine learning algorithm for activity-aware demand response considering energy savings and comfort requirements

Yue Zhang, Anurag. K. Srivastava, Diane Cook
2020 IET Smart Grid  
Using our proposed novel strategy, resident activities are recognized in realtime through a random forest machine learning approach.  ...  Residents are reluctant to participate, and DR controllers lack sufficient real-time activity information to balance energy savings with residents' need for comfort and convenience.  ...  Bryan Minor for his help with the AR system and Dr. Farshid Shariatzadeh for help with the robust HVAC controller.  ... 
doi:10.1049/iet-stg.2019.0249 fatcat:tebvwose4bevnpyuoeyilep4im

Electricity Consumption Forecasting for Smart Grid using the Multi-Factor Back-Propagation Neural Network [article]

Hao Song, Yu Chen, Ning Zhou, Genshe Chen
2019 arXiv   pre-print
In this paper, BPNN is adopted to forecast the electricity consumption using Pecan Street, a community with a relatively large-scale smart grid, as a case study, and takes multiple factors into account  ...  With the development of modern information technology (IT), a smart grid has become one of the major components of smart cities.  ...  with the project, through the methods such as green button protocols, smart meters, home energy monitoring system and so on.  ... 
arXiv:1902.10823v1 fatcat:uthcy3ypxnfsta3ldsriredveu

Highly-accurate short-term forecasting photovoltaic output power architecture without meteorological observations in smart grid

Jun Matsumoto, Daisuke Ishii, Satoru Okamoto, Eiji Oki, Naoaki Yamanaka
2011 2011 1st International Symposium on Access Spaces (ISAS)  
Our proposed method is based on not meteorological observation data but the actual measured output power data by using the solar panels connected with a smart meter as sensing units.  ...  We propose a forecasting architecture of near future photovoltaic output power based on the multipoint output power data via smart meter.  ...  Moreover, a stable power supply systems can be structured by coordinated operation with diesel engine generator based on forecasting photovoltaic output power in micro-grid technology [2] , [3] .  ... 
doi:10.1109/isas.2011.5960945 fatcat:hq2p4m5apbd45putur4ntqasx4

Power System Load Forecasting Based on Dislocation Samples and Cloud Model

Wang Huizhon, Liu Ke, Yang Shiliang, Zhu Hongyi
2017 International Journal of Control and Automation  
Experimental results show that the forecasting accuracy of the new model was significantly higher than the general forecasting methods. Test shows that the prediction method is feasible and effective.  ...  Against short-term load forecasting, this paper established a comprehensive sample system, to evaluate the factors for load forecasting by analyzing the impact of each factor and load data, and select  ...  Introduction With the construction of the smart grid and the marketization of the smart grid further deepen, Not only the higher request for the safe and stable operation of power grid, but also the speed  ... 
doi:10.14257/ijca.2017.10.3.14 fatcat:irwraasb35e55nbkkndnkpg7hy


Asem Alzoubi
2022 International Journal of Computations Information and Manufacturing (IJCIM)  
There has been an increase in the need for intelligence in applications like as asset management, energy-efficient automating, safety, and healthcare monitoring as a result of smart homes coming into existence  ...  The installation of a home energy management system is a practical solution to these issues.  ...  Next, we'll look at how if can be used in smart cities, smart homes, and smart industries with resource-constrained devices.  ... 
doi:10.54489/ijcim.v2i1.75 fatcat:dyrefc362vhfzpszcvvh5cesnu

Short-term Load Forecasting at Different Aggregation Levels with Predictability Analysis [article]

Yayu Peng, Yishen Wang, Xiao Lu, Haifeng Li, Di Shi, Zhiwei Wang, Jie Li
2019 arXiv   pre-print
In addition, the paper also investigates how using data processing improves individual-level residential load forecasting with low predictability.  ...  the predictability using an Irish smart meter dataset.  ...  One feasible approach to increase the accuracy is to incorporate contextual information such as the temperature, humidity, travel plans or even the daily life pattern of residents. A.  ... 
arXiv:1903.10679v1 fatcat:drf2t3wcsrhmnigsghdbqlszeu

Residential Power Load Forecasting

Patrick Day, Michael Fabian, Don Noble, George Ruwisch, Ryan Spencer, Jeff Stevenson, Rajesh Thoppay
2014 Procedia Computer Science  
Advanced smart meters facilitate the deployment of prepaid systems with smart prepaid meters.  ...  The prepaid electric power metering market is being driven in large part by advancements in and the adoption of Smart Grid technology.  ...  System characterization The customer's home residence is equipped with a specially designed prepaid smart meter rather than a standard Radio Frequency (RF) communications enabled smart meter or electromechanical  ... 
doi:10.1016/j.procs.2014.03.056 fatcat:lvy5ihfjabaltoni64zfxbqcly

Deep Learning in Energy Modeling: Application in Smart Buildings with Distributed Energy Generation

Seyed Azad Nabavi, Naser Hossein Motlagh, Martha Arbayani Zaidan, Alireza Aslani, Behnam Zakeri
2021 IEEE Access  
The results demonstrate that the proposed method can forecast building energy demand and energy supply with a high level of accuracy, showing a 3.63-8.57% error range in hourly data prediction for one  ...  In addition, the rescheduling framework decreased the imported electricity cost with the higher electricity tariff by 98 %.  ...  Ku et al. [15] Wind Speed Forecasting Wavelet Transform, Equipping LSTM with DWT significantly in- LSTM, and SVR creases the forecasting accuracy [61]hang et al.[61]optimal scheduling of smart homes' energy  ... 
doi:10.1109/access.2021.3110960 fatcat:43ovxzyai5f2hmz6fc7ysjkdda

Microgrid-Level Energy Management Approach Based on Short-Term Forecasting of Wind Speed and Solar Irradiance

Musaed Alhussein, Syed Irtaza Haider, Khursheed Aurangzeb
2019 Energies  
A smart community that consists of several smart homes and a microgrid is considered.  ...  Methods: We propose an efficient approach for microgrid-level energy management in a smart community based on the integration of DERs and the forecasting wind speed and solar irradiance using a deep learning  ...  A comparison of the proposed model with the smart persistence model is presented in Table 7 .  ... 
doi:10.3390/en12081487 fatcat:bpv6xoorcvg55bulmmh5naqeoe

Scheduling Sensor Duty Cycling Based on Event Detection Using Bi-Directional Long Short-Term Memory and Reinforcement Learning

Muhammad Diyan, Murad Khan, Bhagya Nathali Silva, Kijun Han
2020 Sensors  
things in a smart home environment.  ...  A smart home provides a facilitated environment for the detection of human activity with appropriate Deep Learning algorithms to manipulate data collected from numerous sensors attached to various smart  ...  A network of interconnected systems will be formed where the agent of a home within a smart city will be communicating in parallel with another home and smart grid.  ... 
doi:10.3390/s20195498 pmid:32992795 pmcid:PMC7583935 fatcat:4o7u2jhvfzbqzgih64rodnvg6q

Real-time Load Prediction with High Velocity Smart Home Data Stream [article]

Christoph Doblander and Martin Strohbach and Holger Ziekow and Hans-Arno Jacobsen
2017 arXiv   pre-print
This paper addresses the use of smart-home sensor streams for continuous prediction of energy loads of individual households which participate as an agent in local markets.  ...  We introduces a new device level energy consumption dataset recorded over three years wich includes high resolution energy measurements from electrical devices collected within a pilot program.  ...  We analyze the prediction performance of a broad range of machine learning mechanisms for shortterm load forecasting with smart home data. 3.  ... 
arXiv:1708.04613v1 fatcat:4muru7atqrbwvaat27d3xxyq5m

ICSPIS 2020 Titles

2020 2020 6th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS)  
IN 5G EXPERIENCE APPLICATIONS High Performance Application-oriented Memory Management on Multicore Systems HMM-based Voice Command Detection System in Smart Homes Based on Ontology Method Improving the  ...  learning accuracy in diagnosing diseases using feature selection based on the fruit-fly algorithm Improving the accuracy of Persian Automatic detection of infeasible ALL-DU paths in the data flow test  ... 
doi:10.1109/icspis51611.2020.9349555 fatcat:65hyi64wv5fadg4f5t4fqn4lti
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