This paper presents a novel procedure for short-term load forecasting in distribution management systems. The load is forecasted for feeders that can be of a primarily residential, commercial, industrial or combined type. Each feeder has various amounts of distributed energy resources installed, which accounts for multiple different load patterns. Hence, the distribution management systems cannot use a single short-term load forecasting model for all forecasts. The proposed procedure addresses the specificity of each particular feeder type, by creating customized short-term load forecasting models. It uses a genetic algorithm to select the best inputs for different multiple linear regression models. The genetic algorithm chooses variables from a dataset constructed using load and temperature measurements. The dataset is extended by adding non-linear transformations and mutual interaction effects of the measurements, as well as calendar variables. This extension enables for the modelling of non-linear influences and extracts the non-linearity to the domain of input variables. The models? performance is assessed by the mean absolute percentage error. The proposed procedure is applied to a set of measurements collected from an US electric power utility, which operates in the city of Burbank, Cal., USA. The obtained multiple linear regression model is compared with a previously proposed na?ve benchmark, and a special comparison model, developed by correlation analysis. The proposed method is extendable to suit distribution management systems with different types of electricity consumers.

A HYBRID GENETIC ALGORITHM FOR PARTITIONING OF DATA MODEL IN DISTRIBUTION MANAGEMENT SYSTEMS

Grid computing could be the future computing paradigm for enterprise applications, one of its benefits being that it can be used for executing large scale applications. Utility Management Systems execute very large numbers of workflows with very high resource requirements. This paper proposes architecture for a new scheduling mechanism that dynamically executes a scheduling algorithm using feedback about the current status Grid nodes. Two Artificial Neural Networks were created in order to solve the scheduling problem. A case study is created for the Meter Data Management system with measurements from the Smart Metering system for the city of Novi Sad, Serbia. Performance tests show that significant improvement of overall execution time can be achieved by Hierarchical Artificial Neural Networks.

Machine learning is widely proposed as a basis for smart wastewater treatment plant (WWTP) management, yet reported performance is rarely benchmarked against trivial alternatives. This study uses six years of monitoring from a municipal WWTP in Serbia serving 18,000 population equivalents, comprising 1017 fully measured days. Total nitrogen removal falls from 79.4% outside summer to 67.4% in June to August (p = 4 × 10⁻¹⁸), in five of six years, while influent load, hydraulic flow and phosphorus removal are unchanged, which points to oxygen limitation. Four model families are benchmarked against persistence and a ten-measurement rolling mean on one chronological hold-out period. Predictive skill depends decisively on forecast horizon: at one step ahead nothing beats both baselines, whereas at five steps nitrogen reaches R² of 0.516 against 0.388 and 0.320. Chemical oxygen demand (COD) never beats a rolling mean. A random forest identifies nitrogen exceedance of 15 mg/L with an area under the curve of 0.90. Explanation of that model shows it recognises a breach already under way rather than one beginning, which sets the operating conditions for its use. Evaluating on interpolated rather than measured days raises apparent accuracy from R² of 0.34 to 0.82.

This paper assesses the applicability of client side Internet technologies in software solutions for critical infrastructure systems (CIS). It contains an in-depth analysis of four significant and well known development platforms, namely JavaScript with jQuery, the Google Web Tookit, Microsoft’s Silverlight and Adobe’s Flash/Flex. They were compared by using the ISO software quality characteristics as comparison criteria. Each of the technologies was applied in a real-life project and the results summarize the authors’ experience. The ultimate goal of this research is to enable software engineers to more easily choose a client-side Internet technology when developing a new software solution for the CIS domain.