Artificial neural network-based forecast for electricity consumption in Malaysia

2017 IEEE Symposium Series on Computational Intelligence (SSCI)

et al. 2017

Electrical energy consumption forecasting is, nowadays, essential in order to deal with the new paradigm of consumers' active participation in the power and energy system. The uncertainty related to the variability of consumption is associated to numerous factors, such as consumers' habits, the environmental temperature, luminosity, etc. Current forecasting methods are not suitable to deal with such a combination of input variables, with often highly variable influence on the out-comesthe actual energy consumption. This paper presents a study on the application of five different methods based on fuzzy rulebased systems. This type of method is able to find associations between the distinct input variables, thus creating rules that support and improve the actual forecasting process. A case study is presented, showing the results of applying these five methods to predict the consumption of a real building: the Thales TRT building, in France.

Section: Introductionmentioning

confidence: 99%

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

2017 IEEE Symposium Series on Computational Intelligence (SSCI)

et al. 2017

Section: Introductionmentioning

confidence: 99%

“…The forecast of electricity consumption is performed with the actual data from 1980 to 2004, and results suggest that this is an accurate method to predict the electricity consumption. The forecast of electricity consumption in Malaysia is presented in [4], based on Artificial Natural Networks (ANN). The work presented in [5] demonstrates the forecast of electricity consumption by separating the periodic variable and decompositions the pattern.…”

Section: Introductionmentioning

confidence: 99%

Energy consumption forecasting based on Hybrid Neural Fuzzy Inference System

2016 IEEE Symposium Series on Computational Intelligence (SSCI)

et al. 2016

Forecasting the electricity consumption is one of the most challenging tasks for energy domain stakeholders. Having reliable electricity consumption forecasts can help minimizing the cost of electricity and also enable a better control on the electricity tariff. This paper presents a study regarding the forecast of electricity consumption using a methodology based on Hybrid neural Fuzzy Inference System (HyFIS). The proposed approach considers two distinct strategies, namely one strategy using only the electricity consumption as the input of the method, and the second strategy uses a combination of the electricity consumption and the environmental temperature as the input. A case study considering the forecasting of the consumption of an office building using the proposed methodologies is also presented. Results show that the second strategy is able to achieve better results, hence concluding that HyFIS is an appropriate approach to incorporate different sources of information. In this way, the environmental temperature can help the HyFIS method to achieve a more reliable forecast of the electricity consumption.

“…by supporting the management of reserved electricity to be used in emergency scenarios. In this way, the generation cost for electricity can be minimized, and dynamic electricity tariffs that bring the most advantages out of consumers' participation can be defined [5]. Energy consumption forecasts can also reduce the energy waste, control the energy reserve and master the energy consumption trend [6].…”

Section: Introductionmentioning

confidence: 99%

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

2016 Global Information Infrastructure and Networking Symposium (GIIS)

et al. 2016

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.