One of the critical factors that drive the economic development of a country
and guarantee the sustainability of its industries is the constant availability
of electricity. This is usually provided by the national electric grid.
However, in developing countries where companies are emerging on a constant
basis including telecommunication industries, those are still experiencing a
non-stable electricity supply. Therefore, they have to rely on generators to
guarantee their full functionality. Those generators depend on fuel to function
and the rate of consumption gets usually high, if not monitored properly.
Monitoring operation is usually carried out by a (non-expert) human. In some
cases, this could be a tedious process, as some companies have reported an
exaggerated high consumption rate. This work proposes a label assisted
autoencoder for anomaly detection in the fuel consumed by power generating
plants. In addition to the autoencoder model, we added a labelling assistance
module that checks if an observation is labelled, the label is used to check
the veracity of the corresponding anomaly classification given a threshold. A
consensus is then reached on whether training should stop or whether the
threshold should be updated or the training should continue with the search for
hyper-parameters. Results show that the proposed model is highly efficient for
reading anomalies with a detection accuracy of $97.20\%$ which outperforms the
existing model of $96.1\%$ accuracy trained on the same dataset. In addition,
the proposed model is able to classify the anomalies according to their degree
of severity.

提出了基于标签辅助的自动编码器模型用于发现发电厂燃料消耗的异常，检测准确率为 97.20％，可以根据程度对异常进行分类，相较于已有模型较优。

动力发电厂异常检测中的标签辅助自编码器

Label Assisted Autoencoder for Anomaly Detection in Power Generation Plants

Things are becoming more advanced as technology advances, and machines now
perform the majority of the manual work. The most often used home appliance is
the washing machine for cloths. Modification and research in this field is
essential since it pertains to the amount of time, water, and electricity
required for washing. In this work, a Fuzzy Logic Controller has been developed
for smart washing machines. The objective of this paper is to optimize the
consumption of electricity, water, and detergent for washing machines. The type
of dirt, volume of clothes, and type of cloth play a vital role in saving
water, electricity, and detergent. However, none of the work on the Fuzzy Logic
Controller provided a design procedure endowed with the specified inputs and
outputs implemented in Python. In this paper, we used the Mamdani approach and
created an algorithm based on multi-input multi-output. The algorithm is
implemented in Python. The results of this simulation show that the washing
machine provides better execution at a low computation cost.

本文开发了一种 Python 实现的模糊逻辑控制器，其应用在智能洗衣机领域，旨在优化洗衣机的用电、用水和用洗涤剂的消耗。经过模拟实验表明，该算法能够以较低的计算成本实现更好的洗涤效果。

洗衣机经济模糊逻辑控制器的设计

Design of Economical Fuzzy Logic Controller for Washing Machine

Electricity is a volatile power source that requires great planning and
resource management for both short and long term. More specifically, in the
short-term, accurate instant energy consumption forecasting contributes greatly
to improve the efficiency of buildings, opening new avenues for the adoption of
renewable energy. In that regard, data-driven approaches, namely the ones based
on machine learning, are begin to be preferred over more traditional ones since
they provide not only more simplified ways of deployment but also state of the
art results. In that sense, this work applies and compares the performance of
several deep learning algorithms, LSTM, CNN, mixed CNN-LSTM and TCN, in a real
testbed within the manufacturing sector. The experimental results suggest that
the TCN is the most reliable method for predicting instant energy consumption
in the short-term.

本文研究了如何在短期内准确预测能源消耗，对比了使用 LSTM、CNN、混合 CNN-LSTM 和 TCN 等多种深度学习算法的表现，通过实验结果表明 TCN 是最可靠的预测短期能源消耗的方法。

制造业短期瞬时能耗预测的深度学习

Deep Learning for Short-term Instant Energy Consumption Forecasting in the Manufacturing Sector

Electricity is one of the mandatory commodities for mankind today. To address
challenges and issues in the transmission of electricity through the
traditional grid, the concepts of smart grids and demand response have been
developed. In such systems, a large amount of data is generated daily from
various sources such as power generation (e.g., wind turbines), transmission
and distribution (microgrids and fault detectors), load management (smart
meters and smart electric appliances). Thanks to recent advancements in big
data and computing technologies, Deep Learning (DL) can be leveraged to learn
the patterns from the generated data and predict the demand for electricity and
peak hours. Motivated by the advantages of deep learning in smart grids, this
paper sets to provide a comprehensive survey on the application of DL for
intelligent smart grids and demand response. Firstly, we present the
fundamental of DL, smart grids, demand response, and the motivation behind the
use of DL. Secondly, we review the state-of-the-art applications of DL in smart
grids and demand response, including electric load forecasting, state
estimation, energy theft detection, energy sharing and trading. Furthermore, we
illustrate the practicality of DL via various use cases and projects. Finally,
we highlight the challenges presented in existing research works and highlight
important issues and potential directions in the use of DL for smart grids and
demand response.

本论文综合调查了深度学习在智能电网和需求响应中的应用，主要包括负载预测、状态估计、能量盗窃检测、能源共享与交易等方面，并提出了如何充分利用深度学习技术应对该领域中的重要问题和潜在方向。