MACHINE learningINDEPENDENT variablesENERGY policySUSTAINABILITYENERGY securityBOOSTING algorithmsEnergy demand forecasting is a fundamental aspect of modern energy management. It impacts resource planning, economic stability, environmental sustainability, and energy security. This importance is making it critic...
As the renewable power industry has abundant data that can be exploited in renewable energy forecasting, machine learning techniques can revolutionize the way we deal with renewable energy. This paper describes the efficiency of Linear Regression, Neural Networks Regression, Random Forest Regression, and...
Demand forecasting in DHC-network using machine learning models for e-Energy 2017 by Anamitra R. Choudhury
Machine Learning Models for Electricity Consumption Forecasting: A Review The prediction of energy consumption is a task that allows energy supply companies to adapt to certain behaviors. Among these activities that companies can... A Gonzalez-Briones,G Hernandez,JM Corchado,... 被引量: 0发表: ...
At present, studies on energy demand forecasting are mainly divided into two categories, namely, the white box model and the black box model [11]. The white box model is based on the physical method and requires numerous detailed attributes. The black box models mainly use machine learning met...
Yang, S.X., Li, N.: Power demand forecast based on optimized neural networks by improved Genetic Algorithm. In: Machine Learning and Cybernetics, International Conference, pp. 2877–2881. IEEE (2006) Sözen, A., Arcaklioglu, E.: Prediction of net energy consumption based on economic indic...
A new machine learning approach for price modeling is proposed. The use of neural networks as an advanced signal processing tool may be successfully used to model and forecast energy commodity prices, such as crude oil, coal, natural gas, and electricity prices. Energy commodities have shown expl...
Deep learning (DL). Machine learning subfield that is based on neural networks with representation learning. Generalization The ability to adapt to new, unseen data, drawn from the same distribution as the one used to create the model.
This research has utilised Microsoft Azure Machine Learning Studio, which is a web service solution for the development of prediction model. Starting from data analysis until performance evaluation, AzureML has been successfully employed for the implementation of energy demand forecasting. A major advanta...
(solar radiation, wind) supply energy intermittently due to changes of weather. Therefore, renewable energy sources need to be integrated by a suitable solution design. Time-series forecasting is typically applied to energy management [121] but other machine learning methods are applied as well [30...