The relationship between energy demand and variables such as economic
activity and weather is well established. However, this paper aims to explore
the connection between energy demand and other social aspects, which receive
little attention. Through the use of natural language processing on a large
news corpus, we shed light on this important link. This study was carried out
in five regions of the UK and Ireland and considers multiple horizons from 1 to
30 days. It also considers economic variables such as GDP, unemployment and
inflation. We found that: 1) News about military conflicts, transportation, the
global pandemic, regional economics, and the international energy market are
related to electricity demand. 2) Economic indicators are more important in the
East Midlands and Northern Ireland, while social indicators are more useful in
the West Midlands and the South West of England. 3) The use of these indices
improved forecasting performance by up to 9%.

通过对大量新闻语料库进行自然语言处理，本研究探讨了能源需求与社会其他方面的联系，并发现军事冲突、交通运输、全球大流行、地区经济和国际能源市场等因素与电力需求有关。此外，研究还发现在英国的五个地区，经济指标对于东米德兰兹和北爱尔兰而言更为重要，而社会指标对于西米德兰兹和英格兰西南部更为有用。此外，利用这些指标可以提高预测性能最多达到 9%。

经济和社会因素对能源需求的影响调查

Investigation of the Impact of Economic and Social Factors on Energy  Demand through Natural Language Processing

Forecasting building energy consumption has become a promising solution in
Building Energy Management Systems for energy saving and optimization.
Furthermore, it can play an important role in the efficient management of the
operation of a smart grid. Different data-driven approaches to forecast the
future energy demand of buildings at different scale, and over various time
horizons, can be found in the scientific literature, including extensive
Machine Learning and Deep Learning approaches. However, the identification of
the most accurate forecaster model which can be utilized to predict the energy
demand of such a building is still challenging.In this paper, the design and
implementation of a data-driven approach to predict how forecastable the future
energy demand of a building is, without first utilizing a data-driven
forecasting model, is presented. The investigation utilizes a historical
electricity consumption time series data set with a half-hour interval that has
been collected from a group of residential buildings located in the City of
London, United Kingdom

利用数据驱动方法预测建筑能源需求的可预测性，不需先使用数据驱动的预测模型。

从时间序列数据预测底层建筑用电需求的可预测性

The Forecastability of Underlying Building Electricity Demand from Time  Series Data

The integration of renewable energy sources into the power grid is becoming
increasingly important as the world moves towards a more sustainable energy
future. However, the intermittent nature of renewable energy sources can make
it challenging to manage the power grid and ensure a stable supply of
electricity. In this paper, we propose a deep learning-based approach for
predicting energy demand in a smart power grid, which can improve the
integration of renewable energy sources by providing accurate predictions of
energy demand. We use long short-term memory networks, which are well-suited
for time series data, to capture complex patterns and dependencies in energy
demand data. The proposed approach is evaluated using four datasets of
historical energy demand data from different energy distribution companies
including American Electric Power, Commonwealth Edison, Dayton Power and Light,
and Pennsylvania-New Jersey-Maryland Interconnection. The proposed model is
also compared with two other state of the art forecasting algorithms namely,
Facebook Prophet and Support Vector Regressor. The experimental results show
that the proposed REDf model can accurately predict energy demand with a mean
absolute error of 1.4%. This approach has the potential to improve the
efficiency and stability of the power grid by allowing for better management of
the integration of renewable energy sources.

本研究探讨使用基于深度学习的模型（REDF）来预测能源需求，以提高可再生能源的整合，并与 Facebook Prophet 和支持向量回归等算法进行了比较，结果表明 REDF 模型能以 1.4% 的平均绝对误差准确预测能源需求。