Review of low voltage load forecasting: Methods, applications, and recommendations
The increased digitalisation and monitoring of the energy system opens up numerous
opportunities to decarbonise the energy system. Applications on low voltage, local networks …
opportunities to decarbonise the energy system. Applications on low voltage, local networks …
[HTML][HTML] Large-scale grid integration of residential thermal energy storages as demand-side flexibility resource: A review of international field studies
Power imbalances from fluctuating renewable electricity generators are counteracted by
often expensive flexibility services. Heating, cooling, and air-conditioning (HVAC) of …
often expensive flexibility services. Heating, cooling, and air-conditioning (HVAC) of …
[HTML][HTML] A combined deep learning application for short term load forecasting
An accurate prediction of buildings' load demand is one of the most important issues in
smart grid and smart building applications. In this way, an important contribution is made to …
smart grid and smart building applications. In this way, an important contribution is made to …
Short-term wind speed forecasting approach using ensemble empirical mode decomposition and deep Boltzmann machine
In the recent past, significant growth in renewable generation and integration with grid have
resulted in diversified experiences for planning and operation of modern electric power …
resulted in diversified experiences for planning and operation of modern electric power …
Smart meters and consumer behaviour: Insights from the empirical literature
J Batalla-Bejerano, E Trujillo-Baute, M Villa-Arrieta - Energy Policy, 2020 - Elsevier
This paper summarises the insights to be gained from a systematic literature review of
empirical research devoted to behavioural considerations associated with the use of smart …
empirical research devoted to behavioural considerations associated with the use of smart …
Prediction and explanation of the formation of the Spanish day-ahead electricity price through machine learning regression
G Díaz, J Coto, J Gómez-Aleixandre - Applied Energy, 2019 - Elsevier
Until recently, detailed information on the power system state to estimate future spot prices
by regression analysis was generally restricted to qualified parties. However, to ensure …
by regression analysis was generally restricted to qualified parties. However, to ensure …
Flattening the electricity consumption peak and reducing the electricity payment for residential consumers in the context of smart grid by means of shifting optimization …
Nowadays, by means of smart meters and sensors, more and more electricity consumers
can shift the operation of some of their appliances thus, reducing the electricity expenses …
can shift the operation of some of their appliances thus, reducing the electricity expenses …
Wind power forecasting based on time series model using deep machine learning algorithms
V Chandran, CK Patil, AM Manoharan, A Ghosh… - Materials Today …, 2021 - Elsevier
Wind energy is a created due the uneven heating of the earth surface and Coriolis
acceleration. Wind energy source is capable of continuously and sustainably producing …
acceleration. Wind energy source is capable of continuously and sustainably producing …
The resilient smart city model–proposal for Polish cities
The smart city (SC) concept is currently one of the leading ideas in the field of management.
It has also become important for Polish cities in terms of sustainable development …
It has also become important for Polish cities in terms of sustainable development …
Experimental flexibility identification of aggregated residential thermal loads using behind-the-meter data
Thermal loads are an important source of flexibility at a residential customer level. The
uncertain economic value of residential demand response (DR), and the rising customer …
uncertain economic value of residential demand response (DR), and the rising customer …