相关论文: An evaluation of time series forecasting models on…
Renewable electricity generation has grown significantly across many European power systems, leading to a greener energy mix, but also additional complexity in balancing electricity supply and demand. Unexpected differences between…
Machine and statistical learning algorithms can be reliably automated and applied at scale. Therefore, they can constitute a considerable asset for designing practical forecasting systems, such as those related to urban water demand.…
Irrigation decision systems and water need models have been important research topics in agriculture since 90s. They improve the efficiency of crop yields, provide an appropriate use of water on the earth and so, prevent the water scarcity…
Advancements in smart metering technologies have significantly improved the ability to monitor and manage water utilities. In the context of increasing uncertainty due to climate change, securing water resources and supply has emerged as an…
Complex agricultural problems concern many countries, as the economic motives are increasingly higher, and at the same time the consequences from the irrational resources use and emissions are becoming more evident. In this work we study…
Despite the coupled nature of water and electricity demand, the two utilities are often managed by different entities with minimal interaction. Neglecting the water-energy demand nexus leads to to suboptimal management decisions,…
Earth, water, air, food, shelter and energy are essential factors required for human being to survive on the planet. Among this energy plays a key role in our day to day living including giving lighting, cooling and heating of shelter,…
Water demand is a highly important variable for operational control and decision making. Hence, the development of accurate forecasts is a valuable field of research to further improve the efficiency of water utilities. Focusing on…
Forecasting building energy consumption has become a promising solution in Building Energy Management Systems for energy saving and optimization. Furthermore, it can play an important role in the efficient management of the operation of a…
The accuracy of the household electricity consumption forecast is vital in taking better cost effective and energy efficient decisions. In order to design accurate, proper and efficient forecasting model, characteristics of the series have…
Socio-economic characteristics are influencing the temporal and spatial variability of water demand - the biggest source of uncertainties within water distribution system modeling. Improving our knowledge on these influences can be utilized…
With exponential growth in the human population, it is vital to conserve natural resources without compromising on producing enough food to feed everyone. Doing so can improve people's livelihoods, health, and ecosystems for the present and…
This study describes an investigation into the modelling of citywide water consumption in London, Canada. Multiple modelling techniques were evaluated for the task of univariate time series forecasting with water consumption, including…
Accurate water consumption forecasting is a crucial tool for water utilities and policymakers, as it helps ensure a reliable supply, optimize operations, and support infrastructure planning. Urban Water Distribution Networks (WDNs) are…
The energy-water demands of metropolitan regions and agricultural ecosystems are ever-increasing. To tackle this challenge efficiently and sustainably, the interdependence of these interconnected resources has to be considered. In this…
We present an open-source solution for the operational control of drinking water distribution networks which accounts for the inherent uncertainty in water demand and electricity prices in the day-ahead market of a volatile deregulated…
As a result of increasing population and globalization, the demand for energy has greatly risen. Therefore, accurate energy consumption forecasting has become an essential prerequisite for government planning, reducing power wastage and…
Forecasting groups of time series is of increasing practical importance, e.g. forecasting the demand for multiple products offered by a retailer or server loads within a data center. The local approach to this problem considers each time…
Time-series forecasting plays an important role in many domains. Boosted by the advances in Deep Learning algorithms, it has for instance been used to predict wind power for eolic energy production, stock market fluctuations, or motor…
Prediction of dynamic environmental variables in unmonitored sites remains a long-standing challenge for water resources science. The majority of the world's freshwater resources have inadequate monitoring of critical environmental…