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https://www.iieta.org/journals/ijsdp/paper/10.18280/ijsdp.150417
To assess the results obtained (see Table 1), we construct the dependences of the average electric load at maximum hours for 16 household consumers Figures 4-6. For a constant value, we take the normalized value set in accordance with [21, 22]. ... The obtained method of forecasting power consumption by household consumers, taking into account ...
https://www.researchgate.net/publication/330474705_Forecasting_Residential_Energy_Consumption_Single_Household_Perspective
significantly to the MAPE errors. T ake home#1 for example, for the period of three years. (2014-2016, totally 26304 hours), 77.2% of hours consume. less than two kWh electricity (Fig 4 (a)). The ...
https://www.researchgate.net/publication/270053233_The_Daily_and_Hourly_Energy_Consumption_and_Load_Forecasting_Using_Artificial_Neural_Network_Method_A_Case_Study_Using_a_Set_of_93_Households_in_Portugal
The results show that the ANN approach provides a reliable model for forecasting household electric energy consumption and load profile. ... For each household it was us ed 504 hours, 2/3 of those ...
https://utilityanalytics.com/2021/06/probabilistic-electricity-consumption-forecasting/
Incorporating the full range of possible future consumption values allows us to have a more informed, more flexible forecast. Figure 2: Example of a Probabilistic Forecast. Hourly consumption for a London household. For this example, we estimate that actual future consumption has a 70% chance of falling between .15 and .6 kWh at its forecast peak.
https://github.com/RichaChimnani/Forecast-Model-Electricity-Consumption
Household Power Consumption Dataset. The Household Power Consumption dataset is a multivariate time series dataset that describes the electricity consumption for a single household over four years. The data was collected between December 2006 and November 2010 and observations of power consumption within the household were collected every minute.
https://www.mdpi.com/1996-1073/13/10/2672/htm
Short-term load forecasting is integral to the energy planning sector. Various techniques have been employed to achieve effective operation of power systems and efficient market management. We present a scalable system for day-ahead household electrical energy consumption forecasting, named HousEEC. The proposed forecasting method is based on a deep residual neural network, and integrates ...
https://towardsdatascience.com/energy-consumption-time-series-forecasting-with-python-and-lstm-deep-learning-model-7952e2f9a796
The main objective of the deep learning algorithm for a given time series is to find a function f such that:. Yₜ = f(Yₜ₋₁, Yₜ₋₂, …, Yₜ₋ₚ) In other words, we want to estimate a function that explains the current values of energy consumption based on p lags of the same energy consumption.
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