Towards data-driven energy communities: A review of open-source datasets, models and tools
Energy communities will play a central role in the sustainable energy transition by helping
inform and engage end users to become more responsible consumers of energy. However …
inform and engage end users to become more responsible consumers of energy. However …
Solar-TK: A data-driven toolkit for solar PV performance modeling and forecasting
Solar energy capacity is continuing to increase. The key challenge with integrating solar into
buildings and the electric grid is its high power generation variability, which is a function of …
buildings and the electric grid is its high power generation variability, which is a function of …
Bess aided renewable energy supply using deep reinforcement learning for 5g and beyond
The year of 2020 has witnessed the unprecedented development of 5G networks, along with
the widespread deployment of 5G base stations (BSs). Nevertheless, the enormous energy …
the widespread deployment of 5G base stations (BSs). Nevertheless, the enormous energy …
On efficient operation of a V2G-enabled virtual power plant: when solar power meets bidirectional electric vehicle charging
Virtual power plants (VPP) can increase reliability and efficiency of power systems with a
high share of renewables. However, their adoption largely depends on their profitability …
high share of renewables. However, their adoption largely depends on their profitability …
Learning-aided framework for storage control facing renewable energy
The Internet of Things (IoT) enables reliable and fast data collection and transmission,
providing key infrastructure for power generation, distribution, and control in the smart grid …
providing key infrastructure for power generation, distribution, and control in the smart grid …
Adaptive control of plug-in electric vehicle charging with reinforcement learning
A Al Zishan, MM Haji, O Ardakanian - Proceedings of the eleventh ACM …, 2020 - dl.acm.org
This paper proposes an adaptive additive-increase multiplicative-decrease (AIMD)-like
algorithm for controlled charging of plug-in electric vehicles in a power system. The …
algorithm for controlled charging of plug-in electric vehicles in a power system. The …
Peak forecasting for battery-based energy optimizations in campus microgrids
Battery-based energy storage has emerged as an enabling technology for a variety of grid
energy optimizations, such as peak shaving and cost arbitrage. A key component of battery …
energy optimizations, such as peak shaving and cost arbitrage. A key component of battery …
Research on reducing energy consumption cost of 5G Base Station based on photovoltaic energy storage system
G Ye - 2021 IEEE International Conference on Computer …, 2021 - ieeexplore.ieee.org
At present, 5G technology has good universality and future development prospects.
However, behind 5G's huge potential, its energy consumption has been one of the problems …
However, behind 5G's huge potential, its energy consumption has been one of the problems …
Lightweight online scheduling for home energy management systems under uncertainty
The increasing use of renewable energy sources and electrical energy storage systems
creates a new energy paradigm for residential houses and buildings. Such design reduces …
creates a new energy paradigm for residential houses and buildings. Such design reduces …
Adaptive control using machine learning for distributed storage in microgrids
RR Kolluri, J de Hoog - Proceedings of the Eleventh ACM International …, 2020 - dl.acm.org
The falling costs of solar photovoltaic systems and energy storage mean that these are
being increasingly deployed in microgrids across the globe. Distributed storage can provide …
being increasingly deployed in microgrids across the globe. Distributed storage can provide …