Wang and Mendel's fuzzy rule learning method for energy consumption forecasting considering the influence of environmental temperature

Abstract

Reliable consumption forecasts are crucial in several aspects of power and energy systems, e.g. to take advantage of the full potential of flexibility from consumers and to support the management from operators. With this need, several methodologies for electricity forecasting have emerged. However, the study of correlated external variables, such as temperature or luminosity, is still far from adequate. This paper presents the application of the Wang and Mendel's Fuzzy Rule Learning Method (WM) to forecast electricity consumption. The proposed approach includes two distinct strategies, the first one uses only the electricity consumption as the input of the method, and the second strategy considers a combination of the electricity consumption and the environmental temperature as the input, in order to extract value from the correlation between the two variables. A case study that considers the forecast of the energy consumption of a real office building is also presented. Results show that the WM method using the combination of energy consumption data and environmental temperature is able to provide more reliable forecasts for the energy consumption than several other methods experimented before, namely based on artificial neural networks and support vector machines. Additionally, the WM approach that considers the combination of input values achieves better results than the strategy that considers only the consumption history, hence concluding that WM is appropriate to incorporate different information sources.

Energy consumption forecasting using neuro-fuzzy inference systems: Thales TRT building case study

“…In [14] and [20] two case studies using HyFIS and WM methods to forecast the energy consumption of an office building for a set of 12 hours of an official day have been presented. The average MAPE error in these two studies is 12.42% for HyFIS and 18.41% for WM, while in this work the average MAPE error for HyFIS is 3.45% and for WM is 3.82%.…”

Genetic fuzzy rule-based system using MOGUL learning methodology for energy consumption forecasting

“…The objective of this study is to forecast a better profile of the energy consumption for the following hours. Results from the proposed approach are compared to those achieved in previous studies, using different techniques, namely two fuzzy based systems: A Hybrid Neural Fuzzy Inference System (HyFIS) (Jozi et al, 2016a), and the Wang and Mendel's Fuzzy Rule Learning Method (WM) (Jozi et al, 2016b)e.g. to take advantage of the full potential of flexibility from consumers and to support the management from operators.…”

“…Many works have been published about forecasting the electricity consumption based on fuzzy rules methods. The work presented in (Jozi et al, 2016a) studies about the forecasting of electricity consumption based on the Hybrid Neural Fuzzy Interface System (HyFIS), and in the (Jozi et al, 2016b)e.g. to take advantage of the full potential of flexibility from consumers and to support the management from operators.…”

“…FR.MOGUL method by the average of 9.54% has a better performance than the other two fuzzy rule based methods. (Jozi et al, 2016a), WM (Jozi et al, 2016b)e.g. to take advantage of the full potential of flexibility from consumers and to support the management from operators.…”

Revisión sistemática de literatura sobre modelos de pronósticos de consumo de energía eléctrica

“…El constante incremento del consumo de energía eléctrica (CEE) [1][2], estrechamente ligado al desarrollo socioeconómico [3][4], se ha convertido en uno de los temas que más ocupa la atención de políticos y científicos, tanto para determinar políticas de energía [3][4][5] como para considerar la disminución de costos en todos los puntos de su proceso de producción (generación, distribución y consumo, con énfasis en este último) [6][7] y preservar el medio ambiente [8][9]. La marcada tendencia a la desregulación del mercado de la energía eléctrica [10][11], los cambios climáticos [12][13], la expansión del uso de las energías renovables [14] y la escasez de combustibles fósiles hacen más complejo el escenario; por lo tanto, contar con pronósticos de CEE que den cuenta acabadamente de esta complejidad se vuelve un desafío.…”