Long-Term Energy Demand Forecasting Based on a Systems Analysis

Abstract

— Energy demand forecasting plays a key role in solving the majority of problems connected with determining the economic and energy development prospects. In view of high inertia and capital intensity of energy generation facilities, the changes in the energy consumption structure and rates should be considered for a sufficiently long-term future of no less than 15 years. This adds much difficulty to the development of such forecasts, because it is necessary to take into account the possible results of technological progress in different sectors of the economy and changes in its structure. The suggested approach to energy demand forecasting is based on the systems analysis methods. Energy consumers are disaggregated by economic sectors, country regions, energy carriers, and their utilization areas. As a result, it becomes possible to take into account the possible future technological and structural changes in the economy, its differences in different regions, mutual replacements of energy carriers, and energy saving. The most important feature of the approach is that it involves the separation of economic and energy variables. Economic variables determine the development scales of economic sectors, and energy variables determine the energy consumption intensities in them. The separation of variables helps obtain essentially more accurate forecasts owing to the possibility of taking into account the differences in the variation trends of these variables in the forecast period. For forecasting the economic variables, a nonlinear conditionally dynamic model of interrelations between the economy and energy is used. The energy variables are forecasted on the basis of their links with economic factors, e.g., with cumulative investments in the fixed capital of the considered economic sectors. The discussed approach was implemented as the totality of adaptive simulation models united into the EDFS computing system. The approach was repeatedly and quite successfully used to forecast the energy demand for an up to 10–20-year horizon in solving various problems. The gained experience has demonstrated the correctness of applying the approach for solving long-horizon forecast problems. To illustrate the capacities of the approach, the electric energy demand forecast for the period of up to 2040 for the basic version of the social and economic development of Russia’s economy prepared on the basis of this approach is presented.

Design of an Energy Supply and Demand Forecasting System Based on Web Crawler and a Grey Dynamic Model

“…In the future, we would like to add more Grey Models and other algorithms for more situations, such as machine learning methods [25], seasonal grey model (SGM) [26] and so on [27,28]. Additionally, more features can be involved such as the energy consumer behavior which modelling will be one of the major aspects of future research [29], and the uncertainty of renewable sources, demands, energy market spot prices, etc., which modeling will be a promising line of future research in the modeling of Smart Energy Hub (SEH) [30].…”

Bi-Level Planning Model for Urban Energy Steady-State Optimal Configuration Based on Nonlinear Dynamics

“…Naiming X et al [11] used the gray Markov prediction model to predict China's energy demand while studying the selfsufficiency rate of China's energy demand. Filippov S. P. et al [12] considered the possible technical and structural changes in the future economy, the differences between different regions, the mutual substitution of energy carriers and energy conservation, separated economic variables from energy variables, and made a long-term prediction of urban energy demand. From the perspective of energy coupling, Weijie W et al [13] proposed an energy demand prediction method based on Improved Gray Correlation Analysis and particle swarm optimization BP neural network, which effectively improved the prediction accuracy.…”

Applying Fuzzy Time Series for Developing Forecasting Electricity Demand Models

“…One purpose of electrical markets is to satisfy all the energy needs of each sector and industry [3]. An essential variable for understanding this behavior is the demand for electric energy [4]. This information indicates the amount of energy required for an entity or a series of consumers to meet their needs [5].…”