Introduction

Biomass refers to solid carbonaceous material derived from plants and animals. The energy resulting from biomass is bioenergy. Although bioenergy use is predominant in rural areas, it also provides an important fuel source for the urban poor and many rural, small and medium scale industries. In order to meet the growing demand for energy, it is imperative to focus on efficient production and use of bioenergy to meet fuel requirements [1]. At present, a comprehensive approach to biomass exploitation is required for regions where other kinds of energy are difficult to exploit or where the use of biomass could decrease environmental pollution and enhance regional welfare, e.g., by providing local employment opportunities or improving environmental preservation. The amount and complexity of information relating to the development of bioenergy systems increases and so does the problem of how to handle the information in a manner is helpful for decision making. In this respect, decision support systems (DSS) have been designed to assist in bioresource management at a regional level [2]. Computer models are often complex to use so there has been much effort to develop DSS, which provides the user with an accessible interface with the computer and where the results are presented in a form, which is readily understandable by the user [3]. It is an interactive system that is able to produce data and information and in some cases, even promote understanding related to a given application domain in order to give useful assistance in resolving complex and ill--defined problems [4]. DSS analyzes the collected data and then presents it in a way that can be interpreted by the decision maker. Decision analysis programs can assist the user to create a decision tree of possible decisions and then predict the probabilities and costs of different outcomes. DSS allows ad--hoc enquiries and can assess the probable consequences of decisions before they are made. The DSS analysis tool disintegrates a problem into multiple series of decisions that could be made. Ultimately, the principal distinctive feature of these tools is to enlighten the probability of different outcomes and the expected outcomes. The result of design and execution of DSS for environmental systems, which generate energy, are prolific.

Decision Support Systems (DSS) focus on providing flexible tools for policy analysis, than providing models to answer structured problems [5]. DSS would present the results of a model in a lucid, easily understandable manner to enable policy makers in taking hierarchical sequence of decisions. Most decision support systems are actually based upon mathematical models. Indeed, modelling tools are only a part of DSS, which comprises three component subsystems, namely,

1. The Database Management: Manages an integrated database to drive all models.
2. The Model Management: Helps in creating new models, cataloging and editing existing models, and inter--relates models by links through the spatial and attribute database and integrates small models (building blocks) into larger model systems.
3. The Dialogue Management: Using consistent and familiar interface (like spreadsheet or word processing programs), this design is guided by various methodological considerations, such as,
1. Scenario Approach: Simulates alternative energy and economic futures under different assumptions,
2. Integrated Resource Planning: Stresses the importance of integrating the analyses within a comprehensive planning while emphasizing on a disaggregated approach,
3. Flexibility and User Friendliness: Designed as a set of flexible, expandable and comprehensive modules.

Energy assessment across sectors, through time and localities (region) requires a wide range of information available through a reliable, consistent source. Good information can help to assess various outputs under multiple schemes. Improved technology and growing user acceptance are fueling increased usage demand of DSS for assessment of energy from bioresources. Scope for wide spread use of DSS for bioresources management is its ease of query, reporting, online analysis of both simulated and observed data and speed of processing the data. DSS provides flexible tool for decision makers to assess the effectiveness of the decision through querying and visualisation. Energy savings potential can be observed across different regions.

The world's energy markets rely heavily on fossil fuels such as coal, petroleum, crude oil and natural gas as sources of energy, fuels and chemicals. Since millions of years were required to form fossil fuels in the earth, their reserves are finite and subject to depletion as they are consumed. The only other naturally occurring, energy--containing carbon resource, that is large enough to be used, as a substitute for fossil fuels is biomass. Compared to these, bioresources are renewable with a cycling time less than 100 years. It is the most developed renewable energy source providing 35% and 3% of the primary energy needs of developing and industrialised countries respectively [6]. With 70% of India’s population still in rural areas, there is tremendous demand on resources such as fuelwood, agricultural residues, etc. to meet the daily fuel requirements. About 13.01% of the energy in India is derived from bioresources [7]. Dependence on bioresource to meet the daily requirement of fuel, fodder, etc. in rural areas is more than 85% while in urban area the demand is about 35%.

Biomass is all non--fossil organic materials that have intrinsic chemical energy content. They include aquatic and terrestrial vegetation and all waste biomass such as municipal solid wastes, municipal bio--solids and animal wastes, forestry and agricultural residues and certain types of industrial wastes. Unlike fossil fuels, biomass is renewable in the sense that only a short period of time is needed to replace what is used as an energy resource. Biomass is a renewable energy source because the energy it contains comes from the sun. Through the process of photosynthesis, chlorophyll in plants captures the sun's energy by converting carbon dioxide from the air and water from the ground into carbohydrates, complex compounds composed of carbon, hydrogen and oxygen. When these carbohydrates are burned, they turn back into carbon dioxide and water and release the sun's energy they contain. Bioenergy is regarded as "green" energy for several reasons. Recent study on energy utilisation in Karnataka considering all types of energy sources and sector wise consumption reveals that traditional fuels such as firewood (7.440 million tonnes of oil equivalent--43.62%), agro residues (1.510 million tonnes of oil equivalent--8.85%), biogas and cow dung (0.250 million tonnes of oil equivalent--1.47%) account for 53.20% of total energy consumption [8].

Assessment of available bioresources is helpful in revealing its status and helps in planning a sustained supply to meet the energy demand. Assessment of bioenergy potential can be theoretical, technical or economic. Natural conditions that favor the growth of biomass determine the theoretical potential. Technical potential depends on the available technologies that can be exploited for the conversion of biomass to more flexible forms and so is subjected to change with time. Of all the three potential estimates, the economic potential is subjected to high variability, as economic conditions fluctuate drastically over space and time [9]. To cater these requirements an integrated decision support system is designed that combines sound scientific methods of analysis and assessment of biomass energy (location wise), which can be exploited to meet the regional energy demand in a decentralised way. The assessment also necessitates the validation of DSS with the data compiled for the particular region.

Biomass energy potential assessment (BEPA) Decision Support System provides an integrated framework for easy access of data analysis and the design and evaluation of biomass energy assessment strategies with a unified user interface, comprising of fully menu--driven symbolic/graphic user interface, with a built in context sensitive help features. The distinctive feature of the database is its handling, display and analysis of observation time series data, with a linkage to real time data acquisition and monitoring. It supports realistic analysis and practical simulations of energy assessment.