Archives For Predictive analytics

A new study emphasizing the value of predictive analytics for predicting energy consumption, co-authored with M. Castelli, L. Trujillo, and L. Vanneschi, is scheduled to appear in September 2015 issue of Energy and Buildings. The work titled Prediction of energy performance of residential buildings: A genetic programming approach shows – through an experimental research based on real-data – how utilizing predictive analytics can assist the prediction of energy consumption in residential buildings. Abstract:

Energy consumption has long been emphasized as an important policy issue in today’s economies. In particular, the energy efficiency of residential buildings is considered a top priority of a country’s energy policy. The paper proposes a genetic programming-based framework for estimating the energy performance of residential buildings. The objective is to build a model able to predict the heating load and the cooling load of residential buildings. An accurate prediction of these parameters facilitates a better control of energy consumption and, moreover, it helps choosing the energy supplier that better fits the energy needs, which is considered an important issue in the deregulated energy market. The proposed framework blends a recently developed version of genetic programming with a local search method and linear scaling. The resulting system enables us to build a model that produces an accurate estimation of both considered parameters. Extensive simulations on 768 diverse residential buildings confirm the suitability of the proposed method in predicting heating load and cooling load. In particular, the proposed method is more accurate than the existing state-of-the-art techniques.

Embryonic data mining success

Wednesday, April 22, 2015

A study exploring the critical success factors of embryonic data mining implementation, co-authored with U. Bole, J. Žabkar, G. Papa, and J. Jaklič, has appeared in April 2015 issue of International Journal of Information Management. In the work titled A case analysis of embryonic data mining success we propose and validate, through a series of cases, a conceptual framework to guide practitioners’ adoption of data mining practices. Abstract:

Within highly competitive business environments, data mining (DM) is viewed as a significant technology to enhance decision-making processes by transforming data into valuable and actionable information to gain competitive advantage. There appears, however, to be a dearth of empirical case studies which consider in detail the initial stages in DM management to enable apt foundation for its later successful implementation. Our research applied a multi-method strategy to determine the critical success factors of embryonic DM implementation. We propose and validate, through a series of cases, a conceptual framework to guide practitioners’ adoption of DM. Our findings reveal additional issues for applied decision making in the context of DM success.

Predicting natural disasters

Thursday, April 2, 2015

A new publication emphasizing the role of predictive analytical capabilities in natural disaster management, co-authored with M. Castelli and L. Vanneschi (2015): Predicting Burned Areas of Forest Fires: An Artificial Intelligence Approach appeared in one of the most prestigious international journals in fire ecology field. Abstract:

The ability of accurately predicting forest fire areas may significantly aid optimizing fire management efforts. Given the complexity of the task, powerful computational tools are needed for predicting the amount of area that will be burned during a forest fire. The purpose of this study was to develop an intelligent system based on genetic programming for the prediction of burned areas, using only data related to the forest under analysis and meteorological data. We used geometric semantic genetic programming based on recently defined geometric semantic genetic operators for genetic programming. Experimental results showed the appropriateness of the proposed system for the prediction of the burned areas. In particular, the obtained results were significantly better than those produced by standard genetic programming and other state of the art machine learning methods.