The adverse effects of carbon emissions have garnered significant attention from the international community. Against this backdrop, China has set the dual carbon target and taken concrete actions to achieve it. This study integrates Shared Socioeconomic Pathways with machine-learning...

Effective long-term energy forecasting is essential for efficient management of large institutions like university campuses, yet traditional forecasting methods frequently fall short in capturing complex consumption behaviors. To bridge this gap, this study introduces an advanced machine...

In this paper a new state model is introduced, an adaptative state model in a binary temporal representation (ASMBRT) as well as its application in constructing an algorithmic trading system. The presented model uses the binary temporal representation, which allows for a precise analysis of...

Accurate time-series forecasting of energy consumption and photovoltaic (PV) production is essential for effective energy management and sustainability. Deep Neural Networks (DNNs) are effective tools for learning complex patterns in such data; however, optimizing their architecture remains a...

Innovations in big data and algorithms are enabling new approaches to target interventions at scale. We compare the accuracy of three different systems for identifying the poor to receive benefit transfers - proxy means-testing, nominations from community members, and an algorithmic approach...