Leveraging smart metering solutions to support energy efficiency on the individual household level poses novel research challenges in monitoring usage and providing accurate load forecasting. Forecasting electricity usage is an especially important component that can provide intelligence to smart meters. In this paper, we propose an enhanced approach for load forecasting at the household level....

A novel hybrid data-driven approach is developed for forecasting power system parameters with the goal of increasing the efficiency of short-term forecasting studies for non-stationary time-series. The proposed approach is based on mode decomposition and a feature analysis of initial retrospective data using the Hilbert-Huang transform and machine learning algorithms. The random forests and gra...

Purpose. To propose a method of comparative evaluation technical efficiency mobile workover rig by the criterion life cycle cost. Methodology. The proposed is based on determination specific discounted costs related to condition machine, spent conventional unit work performed. It combines economic and indicators that characterize performance machine. This makes it possible indirectly assess its...

ecological changes resulting from climate conditions can severely affect human societies especially in the area of economy and safety. climate catastrophes may cause social and economic tension. forecasting such changes accurately can help the government to control the disasters and to achieve possible benefits (such as water supply in flood). weather forecasting is the application of science a...

An effective forecasting model for short-term load plays a significant role in promoting the management efficiency of an electric power system. This paper proposes a new forecasting model based on the improved neural networks with random weights (INNRW). The key is to introduce a weighting technique to the inputs of the model and use a novel neural network to forecast the daily maximum load. Ei...

The article is devoted to the problem of applying the formal data mining tool – forecasting – for the developing of new software and for reengineering the present software. We propose the algorithm adjustments of the time series forecasting. This algorithm takes into account the dependence of the current state of time series from the previous one, the influence of basic fuzzy projected trends i...

This paper proposes an efficient and accurate forecasting algorithm for the MDCEV model. The algorithm builds on simple, yet insightful, analytical explorations with the Kuhn-Tucker conditions of optimality that shed new light on the properties of the MDCEV model. For specific, but reasonably general, functional forms of the consumption utility specification, the algorithm circumvents the need ...

Botnets are considered to be among the biggest current threats to global IT infrastructure. Botnets are rapidly evolving and forecasting their survivability and propagation strategies is important for development of countermeasure techniques. Existing malware propagation models mainly concentrate on malware epidemic consequences modeling, i.e. forecasting the number of infected computers, simul...

The prices of financial futures contracts can be interpreted as forecasts of the spot rates, which will apply at the final delivery date of that contract. Financial futures contracts have been traded daily since the early 1980s and provide a substantial bank of data to test the forecasting efficiency of such contracts. Tests are carried out to examine whether the interest rates implied by the f...

Fuzzy Load forecasting plays a paramount role in the operation and management of power systems. Accurate estimation of future power demands for various lead times facilitates the task of generating power reliably and economically. The forecasting of future loads for a relatively large lead time (months to few years) is studied here (long term load forecasting). Among the various techniques used...