One of the main problems in the management of large water supply and distribution systems is the forecasting of daily demand in order to schedule pumping effort and minimize costs. This paper examines methodologies for consumer demand modeling and prediction in a real-time environment for an on-demand irrigation water distribution system. Approaches based on linear multiple regression, univariate time series models (exponential smoothing and ARIMA models), and computational neural networks (CNNs) are developed to predict the total daily volume demand. A set of templates is then applied to the daily demand to produce the diurnal demand profile. The models are established using actual data from an irrigation water distribution system in southern Spain. The input variables used in various CNN and multiple regression models are (1) water demands from previous days; (2) climatic data from previous days (maximum temperature, minimum temperature, average temperature, precipitation, relative humidity, wind speed,...

Green wall irrigation procedures are a particularly important and hard task, given that the quality of the green wall depends on them. There is currently a wide variety of irrigation programmers available, with a range of functions and prices, thereby replacing manual activities and making it easier to maintain green walls. This paper proposes the use of low-cost automated irrigation programmers via a freeware called Arduino. The system is based on air and substrate measurements to ensure optimal plant growth and high water-use efficiency. At certain thresholds, the irrigation system is activated. This not only makes irrigation more convenient but also helps to reduce energy consumption, increases irrigation efficiency and saves time. The data is then sent via Transmission Control Protocol using Internet of Things technology, in this case ThingSpeak. The platform compiles the data and presents them in simple graphical format, thus enabling real-time monitoring from wherever there is Internet access. Together with Arduino, the project incorporates the Raspberry pi system that operates like a database via Hypertext Transfer Protocol Wi-Fi received by a Structured Query Language (MySQL) server using Hypertext Preprocessor. These data are used for the subsequent analysis of green wall performance.