Application of artificial neural networks and fuzzy logic to long-term load forecast considering the price elasticity of electricity demand

Abstract

Summary Over the past few decades, the behavior of electricity consumption has been changing, especially because of improvements in the distributed generation segment and technological innovations presented by smart grids. The use of microgeneration and the availability of electricity pricing in real time allow consumers to control their consumption, or generation, according to market conditions. This new dynamic tends to increasingly change the price elasticity of electricity demand, by indicating the need to readjust load forecasting models. In this market environment, in addition to providing robust estimates for the planning and operation of electric power systems, load forecasting models have become fundamental in the context of demand management. Thus, this paper proposes to develop an artificial neural network and fuzzy logic for load forecasting to perform an efficiency analysis. This system is able to provide estimates of the elasticity of electricity demand behavior with more satisfactory results. To do so, improvements in the neural network with multilayer perceptron are proposed. In this case, the adaptation of parameters to correlate variations in consumption with the changes in electricity tariffs was developed. The addition of this new structure produced better results compared with the conventional neural network. Computer tests were conducted using historical data from the ISO New England Inc and PJM Interconnection. Price elasticity estimates of electricity demand showed a sharp increase of demand in relation to the elasticity behavior.

Optimal BRA based electric demand prediction strategy considering instance‐based learning of the forecast factors

“…The obtained results from Tables 6‐8 verify that optimal‐BRA outperforms and yields satisfactory results than BP‐ANN, LM‐ANN, GA, Jaya‐ANN, PSO, GWO, GOA, SSA, and HHO. A prediction is acceptable if E MAPE is less than 10% 13 . That is why optimal‐BRA is appropriate for real‐time LF applications.…”

“…A prediction is acceptable if E MAPE is less than 10%. 13 That is why optimal-BRA is appropriate for real-time LF applications.…”

“…In contrast, in the evening, most of the electric load is due to lighting. [12][13][14] LF classes, prediction horizons, their influencing factors, and applications have been summarized in Figure 1.…”

“…The more significant electric load is consumed in the early morning due to several activities like bathing and cooking. In contrast, in the evening, most of the electric load is due to lighting 12‐14 . LF classes, prediction horizons, their influencing factors, and applications have been summarized in Figure 1.…”

Short‐term commercial load forecasting based on peak‐valley features with the TSA‐ELM model

“…These include areas such as medicine, 12,13 crime tracking systems, 14 and power load forecasting. 15,16 Researchers prefer artificial neural networks (ANN) in dealing with STLF problems, 17,18 owing to their strong nonlinear learning ability and fault tolerance. 19,20 However, many ANN-based gradient-based methods, such as backpropagation or other variants, have some limitations in the field of electric load forecasting.…”