Deep Learning Applied to Energy Forecast: Implementation and Evaluation

随着大数据成为计算和建模的中心，数据分析工具对于快速发展的人工智能和机器学习领域至关重要。在去年NSERC Engage项目的支持下，我们通过应用人工神经网络的深度学习技术开发了一个能源消耗预测及其预测不确定性的计算模型。下一步，**Engage Plus项目旨在实施和评估新开发的系统模型。我们还将研究b谷歌和微软等大公司使用的替代机器学习算法的最新进展。**该项目的成功将使行业合作伙伴受益，帮助公司采用新的创新技术并升级其现有服务。这项工作将有助于加拿大在创造人工智能技术方面的领导地位，并丰富经济增长。

As big data becomes a center for computing and modelling, tools for data analytics are most important for the**rapid development areas of artificial intelligent and machine learning. With the support of an NSERC Engage**project last year, we have developed a computational model for energy consumption forecasting and its**prediction uncertainty by applying the deep learning techniques of Artificial Neural Network. As next step, the**Engage Plus project aims to implement and evaluate the newly developed system model. We will also**investigate current advances in the alternative machine learning algorithms that have been used by the large**companies like Google and Microsoft.**The success of this project will benefit the industry partner by helping the company to adopt new innovate**technology and upgrade its current services. The work will contribute to the Canadian leadership in the creation**of artificial intelligence technology and enriches the economic growth.