In this paper, we consider energy demand prediction in district heating
systems. Effective energy demand prediction is essential in combined heat power
systems when offering electrical energy in competitive electricity markets. To
address this problem, we propose two sets of algorithms: (1) a novel extension
to the algorithm proposed by E. Dotzauer and (2) an autoregressive predictor
based on hour-of-week adjusted linear regression on moving averages of energy
consumption. These two methods are compared against state-of-the-art artificial
neural networks. Energy demand predictor algorithms have various computational
costs and prediction quality. While prediction quality is a widely used measure
of predictor superiority, computational costs are less frequently analyzed and
their impact is not so extensively studied. When predictor algorithms are
constantly updated using new data, some computationally expensive forecasting
methods may become inapplicable. The computational costs can be split into
training and execution parts. The execution part is the cost paid when the
already trained algorithm is applied to predict something. In this paper, we
evaluate the above methods with respect to the quality and computational costs,
both in the training and in the execution. The comparison is conducted on a
real-world dataset from a district heating system in the northwest part of
Poland.

本文研究了热电联供系统中能源需求的预测问题，提出了两种算法，并与现有的人工神经网络算法进行了比较，主要考虑了算法的预测质量和计算成本。

区域供暖系统能耗预测的质量与速度比较

Quality versus speed in energy demand prediction for district heating systems

In this paper, we propose novel approaches using state-of-the-art machine
learning techniques, aiming at predicting energy demand for electric vehicle
(EV) networks. These methods can learn and find the correlation of complex
hidden features to improve the prediction accuracy. First, we propose an energy
demand learning (EDL)-based prediction solution in which a charging station
provider (CSP) gathers information from all charging stations (CSs) and then
performs the EDL algorithm to predict the energy demand for the considered
area. However, this approach requires frequent data sharing between the CSs and
the CSP, thereby driving communication overhead and privacy issues for the EVs
and CSs. To address this problem, we propose a federated energy demand learning
(FEDL) approach which allows the CSs sharing their information without
revealing real datasets. Specifically, the CSs only need to send their trained
models to the CSP for processing. In this case, we can significantly reduce the
communication overhead and effectively protect data privacy for the EV users.
To further improve the effectiveness of the FEDL, we then introduce a novel
clustering-based EDL approach for EV networks by grouping the CSs into clusters
before applying the EDL algorithms. Through experimental results, we show that
our proposed approaches can improve the accuracy of energy demand prediction up
to 24.63% and decrease communication overhead by 83.4% compared with other
baseline machine learning algorithms.

本文介绍了使用机器学习技术预测电动汽车网络的能量需求的方法，包括一种使用聚类和联邦学习的方法，以减少通信开销和保护数据隐私。实验结果表明，与其他基线机器学习算法相比，我们的方法可以提高能量需求预测的准确性高达 24.63％，并降低 83.4％的通信开销。