Energy Communities as a Beginning of a Successful Energy Transition: Model-Based Forecasting Approach
Energy Communities as a Beginning of a Successful Energy Transition: Model-Based Forecasting Approach

Author(s): Ralena Kutkarska, Dimitrina Koeva
Subject(s): Politics / Political Sciences, Politics, Social Sciences, Economy, National Economy, Business Economy / Management, Energy and Environmental Studies, Sociology, Environmental and Energy policy, Methodology and research technology, Policy, planning, forecast and speculation
Published by: Университет за национално и световно стопанство (УНСС)
Keywords: energy transition; energy communities; energy poverty
Summary/Abstract: All scenarios for the development of the energy sector in Bulgaria, in the context of permanent processes of energy transformation and decarbonization, are subject to a comprehensive planning strategy and a revised organizational structure for the energy market, with adequate management of energy flows in the electricity system and reasoned decisions on investments in new capacities and/or reconstruction of existing ones. Energy community will play a key role in these processes. The questions of what the trends are, what are the necessary actions to overcome regulatory and financial constraints in building energy community can be answered only after an adequate forecast analysis of electricity production and consumption. It is well known that forecasting electricity production and consumption is an important aspect of ensuring optimal functioning of energy systems with guaranteed energy security, planning and thus uninterrupted power supply. The constant demand for electricity, the need for sustainability and guaranteed supply, demonstrates the need for a reliable methodology for forecasting supply and demand in the energy market. In this study, autoregressive integrated moving average (ARIMA) statistical models are used to forecast monthly electricity generation and consumption. ARIMA models are a widely used tool for analyzing and forecasting future trends, based on historical time series data, allowing the identification of patterns and seasonality. Another advantage of these models is that they can be extended to account for various influencing factors. In this way, it is possible to consider the specifics of each energy company.

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