In a paper recently published in the open-access journal Energies, researchers reviewed and summarized published articles to determine the most promising approach for artificial intelligence (AI) applications in environmental and energy engineering systems. AI is a computer science tool that works on creating intelligent devices, machines, and systems that carry out operations akin to human decision-making and learning. It can comprehend external data and learn from it, and adapt to its learning with practice. Combining AI with the internet of things (IoT) technologies could be another promising approach as this combination can harvest large amounts of data, and the AI can study data patterns to enable task automation for several business benefits. AI has been widely utilized in agriculture, focusing particularly on rice diseases, crop and pest management, product monitoring, and yield prediction. Medical and healthcare applications of AI include the understanding of diseases such as cancer as well as brain and heart disorders.

More recently there has been an interest in density forecasts for the hourly prices, motivated by considerations of risk management. See [1,2] for extensive reviews. In this paper, we provide a new formulation with a focus upon price spreads, and specifically, we forecast the density functions for the intraday spreads in the day-ahead prices. The optimal operation of storage facilities, e.g., batteries and electric vehicles, or load shifting programmes, e.g., demand-side management, over daily cycles depends upon these spreads if they are operated as merchants, arbitraging buying and selling from the wholesale market. Furthermore, if the risk is a consideration, analysis of the mean differences in price levels would be inadequate, and we therefore directly estimate the density functions of all hourly spreads in prices at the day-ahead stage. These forecasts ahead of the day-ahead auctions would be needed to help traders decide whether they want to be buyers or sellers at each hour and thereby optimise their bids and offers. Our specification, estimation and forecasting of these arbitrage spreads are new and computationally-intensive. Based upon day-ahead forecasts for the drivers of electricity prices, such as demand, wind and solar production, gas and coal prices, forecasts for electricity price levels have been proposed from various methods, e.g., [3-6] and some for price densities [1,7], but apparently no methods have been developed specifically for forecasting intraday spread densities. Until recently storage assets, such as pumped hydro storage, would regularly store energy at night and discharge at the daily peak demand periods, which were quite predictable. However with the penetration of wind and especially solar generating facilities, the peak and trough hours in prices move around the day and in sunny locations with substantial solar energy, e.g., California, the lowest prices may often be in the middle of the day [8].