A review of the-state-of-the-art in data-driven approaches for building energy prediction
Building energy prediction plays a vital role in developing a model predictive controller for
consumers and optimizing energy distribution plan for utilities. Common approaches for …
consumers and optimizing energy distribution plan for utilities. Common approaches for …
A sequential ensemble model for photovoltaic power forecasting
During this era of the energy crisis, when the non-renewable sources are rapidly
diminishing, efforts are being taken to utilize renewable sources predominantly. This …
diminishing, efforts are being taken to utilize renewable sources predominantly. This …
A short-term energy prediction system based on edge computing for smart city
The development of Internet of Things technologies has provided potential for real-time
monitoring and control of environment in smart cities. In the field of energy management …
monitoring and control of environment in smart cities. In the field of energy management …
[HTML][HTML] A systematic review of data pre-processing methods and unsupervised mining methods used in profiling smart meter data
FM Dahunsi, AE Olawumi, DT Ale… - AIMS Electronics and …, 2021 - aimspress.com
The evolution of smart meters has led to the generation of high-resolution time-series data-a
stream of data capable of unveiling valuable knowledge from consumption behaviours for …
stream of data capable of unveiling valuable knowledge from consumption behaviours for …
Electricity consumption prediction based on LSTM with attention mechanism
Z Lin, L Cheng, G Huang - IEEJ Transactions on Electrical and …, 2020 - Wiley Online Library
Power data analysis in power system, such as electricity consumption prediction, has always
been the basis for the power department to adjust electricity price, substation regulation …
been the basis for the power department to adjust electricity price, substation regulation …
Towards efficient and intelligent internet of things search engine
The Internet of Things (IoT) has created a novel ecosystem for sensing and actuation
throughout our world, enabling intelligently controlled autonomous systems to conserve …
throughout our world, enabling intelligently controlled autonomous systems to conserve …
Electricity consumption forecasting with outliers handling based on clustering and deep learning with application to the Algerian market
The reduction of electricity loss and the effective management of electricity demand are vital
operations for production and distribution electricity enterprises. To achieve these goals …
operations for production and distribution electricity enterprises. To achieve these goals …
Predicting blood glucose using an LSTM neural network
Diabetes self-management relies on the blood glucose prediction as it allows taking suitable
actions to prevent low or high blood glucose level. In this paper, we propose a deep learning …
actions to prevent low or high blood glucose level. In this paper, we propose a deep learning …
A comparative study of LSTM and ARIMA for energy load prediction with enhanced data preprocessing
I Mpawenimana, A Pegatoquet, V Roy… - 2020 IEEE Sensors …, 2020 - ieeexplore.ieee.org
Energy load prediction plays a central role in the decision-making process of energy
production and consumption for smart homes with systems based on energy harvesting …
production and consumption for smart homes with systems based on energy harvesting …
Short-term load forecasting with deep boosting transfer regression
With the increasing popularity of electric vehicles and the growing trend of working from
home, electricity consumption in the residential sector is expected to continue to grow …
home, electricity consumption in the residential sector is expected to continue to grow …