Results 301 to 310 of about 288,600 (392)
Local Ecological Knowledge Reveals the Distribution of Cryptic Nocturnal Wildlife
Many nocturnal animals are difficult to study because they are rarely seen, including nocturnal primates, galagos and pottos, in West Africa. Working with over 600 people in 52 villages in southern Guinea‐Bissau, we found that communities frequently recognized galagos by their red eyeshine and distinctive calls, while pottos were not known.
Chloe Chesney +6 more
wiley +1 more source
Impact of distance measures in adaptive K-means clustering on load profiles and spatial patterns of distributed substations in Thailand. [PDF]
Prompook T +8 more
europepmc +1 more source
ABSTRACT Building energy systems integrating multiple energy sources can effectively reduce energy consumption and facilitate renewable energy integration. Integrating electrical energy storage (EES) into these systems helps accommodate the increasing share of renewables; however, the stochastic and intermittent nature of solar power still poses ...
Zhengtian Wu +9 more
wiley +1 more source
The impact of air conditioning on residential electricity consumption across world countries
Enrica De Cian +4 more
openalex +1 more source
Norwegian energy community dataset: An electricity-hydrogen system with renewables, battery storage & hydrogen demand. [PDF]
Mochi P, Korpås M.
europepmc +1 more source
ABSTRACT With the advancement of smart grid and Internet of Things, alongside broad adoption of distributed energy resources, precise profiling of residential users has become vital to grid operational efficiency and load forecasting accuracy. However, existing profiling approaches mainly rely on single‐source load data and fail to capture the dynamic ...
Danlin Li +6 more
wiley +1 more source
The impact of real-time energy consumption feedback on residential gas and electricity usage
Mirthe Boomsma +2 more
openalex +1 more source
Data collection in solar systems for efficiency enhancement. [PDF]
Coutinho CR +5 more
europepmc +1 more source
ABSTRACT Smart energy management systems (EMS) are entering a phase of rapid transformation. Artificial intelligence (AI)—including machine learning (ML), deep learning (DL), and reinforcement learning (RL)—has become the computational backbone for real‐time forecasting, scheduling, and control of renewable‐rich power systems.
Sihai An +5 more
wiley +1 more source

