Bayesian model of electrical heating disaggregation
- URL: http://arxiv.org/abs/2011.05674v1
- Date: Wed, 11 Nov 2020 10:05:15 GMT
- Title: Bayesian model of electrical heating disaggregation
- Authors: Fran\c{c}ois Culi\`ere, Laetitia Leduc and Alexander Belikov
- Abstract summary: Adoption of smart meters is a major milestone on the path of European transition to smart energy.
The residential sector in France represents $approx$35% of electricity consumption with $approx$40% (INSEE) of households using electrical heating.
The number of deployed smart meters Linky is expected to reach 35M in 2021.
- Score: 68.8204255655161
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Adoption of smart meters is a major milestone on the path of European
transition to smart energy. The residential sector in France represents
$\approx$35\% of electricity consumption with $\approx$40\% (INSEE) of
households using electrical heating. The number of deployed smart meters Linky
is expected to reach 35M in 2021. In this manuscript we present an analysis of
676 households with an observation period of at least 6 months, for which we
have metadata, such as the year of construction and the type of heating and
propose a Bayesian model of the electrical consumption conditioned on
temperature that allows to disaggregate the heating component from the
electrical load curve in an unsupervised manner. In essence the model is a
mixture of piece-wise linear models, characterised by a temperature threshold,
below which we allow a mixture of two modes to represent the latent state
home/away.
Related papers
- Short-Term Electricity Demand Forecasting of Dhaka City Using CNN with Stacked BiLSTM [0.471858286267785]
This paper proposes a hybrid model of Convolutional Neural Network (CNN) and stacked Bidirectional Long-short Term Memory (BiLSTM) architecture to perform an accurate short-term forecast of the electricity demand of Dhaka city.
The proposed approach produced the best prediction results in comparison to the other benchmark models used in the study, with MAPE 1.64%, MSE 0.015, RMSE 0.122 and MAE 0.092.
arXiv Detail & Related papers (2024-06-10T09:02:07Z) - Modeling of Annual and Daily Electricity Demand of Retrofitted Heat
Pumps based on Gas Smart Meter Data [0.0]
Gas furnaces are common heating systems in Europe.
Heat pumps should continuously replace existing gas furnaces.
New approaches are required to estimate the additional electricity demand to operate heat pumps.
arXiv Detail & Related papers (2023-10-04T11:55:04Z) - Electricity Demand Forecasting with Hybrid Statistical and Machine
Learning Algorithms: Case Study of Ukraine [0.0]
The proposed methodology was constructed using hourly data from Ukraine's electricity consumption ranging from 2013 to 2020.
Our hybrid model is very effective at forecasting long-term electricity consumption on an hourly resolution.
arXiv Detail & Related papers (2023-04-11T12:15:50Z) - Comparison of Forecasting Methods of House Electricity Consumption for
Honda Smart Home [0.0]
Electricity consumption forecasting enables the development of home energy management systems.
Energy performance in buildings is influenced by many factors like ambient temperature, humidity, and a variety of electrical devices.
The Honda Smart Home US data set was selected to compare three methods for minimizing forecasting errors.
arXiv Detail & Related papers (2022-08-11T19:04:41Z) - A Hybrid Model for Forecasting Short-Term Electricity Demand [59.372588316558826]
Currently the UK Electric market is guided by load (demand) forecasts published every thirty minutes by the regulator.
We present HYENA: a hybrid predictive model that combines feature engineering (selection of the candidate predictor features), mobile-window predictors and LSTM encoder-decoders.
arXiv Detail & Related papers (2022-05-20T22:13:25Z) - Domain Knowledge Aids in Signal Disaggregation; the Example of the
Cumulative Water Heater [68.8204255655161]
We present an unsupervised low-frequency method aimed at detecting and disaggregating the power used by Cumulative Water Heaters (CWH) in residential homes.
Our model circumvents the inherent difficulty of unsupervised signal disaggregation by using both the shape of a power spike and its time of occurrence.
Our model, despite its simplicity, offers promising applications: detection of mis-configured CWHs on off-peak contracts and slow performance degradation.
arXiv Detail & Related papers (2022-03-22T10:39:19Z) - The impact of online machine-learning methods on long-term investment
decisions and generator utilization in electricity markets [69.68068088508505]
We investigate the impact of eleven offline and five online learning algorithms to predict the electricity demand profile over the next 24h.
We show we can reduce the mean absolute error by 30% using an online algorithm when compared to the best offline algorithm.
We also show that large errors in prediction accuracy have a disproportionate error on investments made over a 17-year time frame.
arXiv Detail & Related papers (2021-03-07T11:28:54Z) - Investigating Underlying Drivers of Variability in Residential Energy
Usage Patterns with Daily Load Shape Clustering of Smart Meter Data [53.51471969978107]
Large-scale deployment of smart meters has motivated increasing studies to explore disaggregated daily load patterns.
This paper aims to shed light on the mechanisms by which electricity consumption patterns exhibit variability.
arXiv Detail & Related papers (2021-02-16T16:56:27Z) - Distributed Deep Reinforcement Learning for Intelligent Load Scheduling
in Residential Smart Grids [9.208362060870822]
We propose a model-free method for the households which works with limited information about the uncertain factors.
We then utilize real-world data from Pecan Street Inc., which contains the power consumption profile of more than 1; 000 households.
In average, the results reveal that we can achieve around 12% reduction on peak-to-average ratio (PAR) and 11% reduction on load variance.
arXiv Detail & Related papers (2020-06-29T15:01:51Z) - A generative adversarial network approach to (ensemble) weather
prediction [91.3755431537592]
We use a conditional deep convolutional generative adversarial network to predict the geopotential height of the 500 hPa pressure level, the two-meter temperature and the total precipitation for the next 24 hours over Europe.
The proposed models are trained on 4 years of ERA5 reanalysis data from 2015-2018 with the goal to predict the associated meteorological fields in 2019.
arXiv Detail & Related papers (2020-06-13T20:53:17Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.