Current global food supply chains are faced with an ever increasing variety of modern day
societal challenges. As a direct result of these challenges many of these supply chains are …

Electrical utilities depend on short-term demand forecasting to proactively adjust production
and distribution in anticipation of major variations. This systematic review analyzes 240 …

This paper compares four practical methods for electricity generation forecasting of grid-
connected Photovoltaic (PV) plants, namely Seasonal Autoregressive Integrated Moving …

Time series modeling is an effective approach for studying and analyzing the future
performance of the power sector based on historical data. This study proposes a forecasting …

Electricity, a fundamental commodity, must be generated as per required utilization which
cannot be stored at large scales. The production cost heavily depends upon the source such …

This article focuses on developing both statistical and machine learning approaches for
forecasting hourly electricity demand in Ontario. The novelties of this study include (i) …

Forecasting is defined as the process of estimating the change in uncertain situations. One
of the most vital aspects of many applications is temperature forecasting. Using the Daily …

Day-ahead electricity load forecasts are presented for the ISO-NE CA area. Four different
methods are discussed and compared, namely seasonal autoregressive moving average …

The accuracy of short-term load forecasting plays an important role in the operation of the
power system. However, because of the randomness of load data, it is a difficult task to …

Although photovoltaic generation has been proposed as a solution for the world's energy
challenges, it depends to a large extent on solar irradiation and air temperature. Therefore …