With the globally increasing electricity demand, its related uncertainties are on the rise as
well. Therefore, a deeper insight of load forecasting techniques for projecting future …

In this article, a systematic literature review of 419 articles on energy demand modeling,
published between 2015 and 2020, is presented. This provides researchers with an …

Accurate estimation of drought events is vital for the mitigation of their adverse
consequences on water resources, agriculture and ecosystems. Machine learning …

Daily reference evapotranspiration (ETo) forecasts can help farmers in irrigation planning.
Therefore, this study assesses the potential of deep learning (long short-term memory …

The forecast of electricity demand has been a recurrent research topic for decades, due to its
economical and strategic relevance. Several Machine Learning (ML) techniques have …

Analyzing the future behaviors of currency pairs represents a priority for governments,
financial institutions, and investors, who use this type of analysis to understand the …

Effective workforce forecasting is critical to strategic management in construction projects,
particularly ensuring staffing is optimized for efficient and timely project completion. This …

Water scarcity is the most obstacle faced by irrigation water requirements, likewise, limited
available meteorological data to calculate reference evapotranspiration. Consequently, the …

Despite the tightening of energy performance standards for buildings in various countries
and the increased use of efficient and renewable energy technologies, it is clear that the …

In this study, the electricity demands of some Fortune 500 companies in Türkiye have been
forecasted by using deep learning techniques. This is a quite harder problem than the …