State-of-the-art short-term electricity market operation with solar generation: A review

Collateral mechanism in the Electricity Market ensures the payments are executed on a timely manner; thus maintains the continuous cash flow. In order to value collaterals, Takasbank, the authorized central settlement bank, creates segments of the market participants by considering their short-term and long-term debt/credit information arising from all market activities. In this study, the data regarding participants’ daily and monthly debt payment and penalty behaviors is analyzed with the aim of discovering high-risk participants that fail to clear their debts on-time frequently. Different clustering techniques along with different distance metrics are considered to obtain the best clustering. Moreover, data preprocessing techniques along with Recency, Frequency, Monetary Value (RFM) scoring have been used to determine the best representation of the data. The results show that Agglomerative Clustering with cosine distance achieves the best separated clustering when the non-normalized dataset is used; this is also acknowledged by a domain expert.

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Short-Term Electricity Price Forecasting by Employing Ensemble Empirical Mode Decomposition and Extreme Learning Machine

Forecasting ◽

10.3390/forecast3030028 ◽

2021 ◽

Vol 3 (3) ◽

pp. 460-477

Author(s):

Sajjad Khan ◽

Shahzad Aslam ◽

Iqra Mustafa ◽

Sheraz Aslam

Keyword(s):

Extreme Learning Machine ◽

Empirical Mode Decomposition ◽

Electricity Market ◽

Ensemble Empirical Mode Decomposition ◽

Electricity Price ◽

Price Forecasting ◽

Short Term ◽

Mode Decomposition ◽

Electricity Price Forecasting ◽

Learning Machine

Day-ahead electricity price forecasting plays a critical role in balancing energy consumption and generation, optimizing the decisions of electricity market participants, formulating energy trading strategies, and dispatching independent system operators. Despite the fact that much research on price forecasting has been published in recent years, it remains a difficult task because of the challenging nature of electricity prices that includes seasonality, sharp fluctuations in price, and high volatility. This study presents a three-stage short-term electricity price forecasting model by employing ensemble empirical mode decomposition (EEMD) and extreme learning machine (ELM). In the proposed model, the EEMD is employed to decompose the actual price signals to overcome the non-linear and non-stationary components in the electricity price data. Then, a day-ahead forecasting is performed using the ELM model. We conduct several experiments on real-time data obtained from three different states of the electricity market in Australia, i.e., Queensland, New South Wales, and Victoria. We also implement various deep learning approaches as benchmark methods, i.e., recurrent neural network, multi-layer perception, support vector machine, and ELM. In order to affirm the performance of our proposed and benchmark approaches, this study performs several performance evaluation metric, including the Diebold–Mariano (DM) test. The results from the experiments show the productiveness of our developed model (in terms of higher accuracy) over its counterparts.

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Short-Term Forecasting of Electricity Supply and Demand by Using the Wavelet-PSO-NNs-SO Technique for Searching in Big Data of Iran’s Electricity Market

Data ◽

10.3390/data3040043 ◽

2018 ◽

Vol 3 (4) ◽

pp. 43 ◽

Cited By ~ 3

Author(s):

Mesbaholdin Salami ◽

Farzad Movahedi Sobhani ◽

Mohammad Ghazizadeh

Keyword(s):

Neural Networks ◽

Electricity Market ◽

Simulation Optimization ◽

Supply And Demand ◽

Electricity Supply ◽

Wavelet Neural Networks ◽

Short Term ◽

Swarm Optimization ◽

Forecasting Accuracy ◽

Short Term Forecasting

The databases of Iran’s electricity market have been storing large sizes of data. Retail buyers and retailers will operate in Iran’s electricity market in the foreseeable future when smart grids are implemented thoroughly across Iran. As a result, there will be very much larger data of the electricity market in the future than ever before. If certain methods are devised to perform quick search in such large sizes of stored data, it will be possible to improve the forecasting accuracy of important variables in Iran’s electricity market. In this paper, available methods were employed to develop a new technique of Wavelet-Neural Networks-Particle Swarm Optimization-Simulation-Optimization (WT-NNPSO-SO) with the purpose of searching in Big Data stored in the electricity market and improving the accuracy of short-term forecasting of electricity supply and demand. The electricity market data exploration approach was based on the simulation-optimization algorithms. It was combined with the Wavelet-Neural Networks-Particle Swarm Optimization (Wavelet-NNPSO) method to improve the forecasting accuracy with the assumption Length of Training Data (LOTD) increased. In comparison with previous techniques, the runtime of the proposed technique was improved in larger sizes of data due to the use of metaheuristic algorithms. The findings were dealt with in the Results section.

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A Two-Layer Long Short-Term Memory Network for Bottleneck Prediction in Multi-Job Manufacturing Systems

Volume 3: Manufacturing Equipment and Systems ◽

10.1115/msec2018-6678 ◽

2018 ◽

Cited By ~ 1

Author(s):

Xingjian Lai ◽

Huanyi Shui ◽

Jun Ni

Keyword(s):

Manufacturing Systems ◽

Short Term Memory ◽

Complex Dynamics ◽

State Of The Art ◽

Short Term ◽

Term Memory ◽

Future Production ◽

Effective Manner ◽

Long Short Term Memory ◽

Factory Floor

Throughput bottlenecks define and constrain the productivity of a production line. Prediction of future bottlenecks provides a great support for decision-making on the factory floor, which can help to foresee and formulate appropriate actions before production to improve the system throughput in a cost-effective manner. Bottleneck prediction remains a challenging task in literature. The difficulty lies in the complex dynamics of manufacturing systems. There are multiple factors collaboratively affecting bottleneck conditions, such as machine performance, machine degradation, line structure, operator skill level, and product release schedules. These factors impact on one another in a nonlinear manner and exhibit long-term temporal dependencies. State-of-the-art research utilizes various assumptions to simplify the modeling by reducing the input dimensionality. As a result, those models cannot accurately reflect complex dynamics of the bottleneck in a manufacturing system. To tackle this problem, this paper will propose a systematic framework to design a two-layer Long Short-Term Memory (LSTM) network tailored to the dynamic bottleneck prediction problem in multi-job manufacturing systems. This neural network based approach takes advantage of historical high dimensional factory floor data to predict system bottlenecks dynamically considering the future production planning inputs. The model is demonstrated with data from an automotive underbody assembly line. The result shows that the proposed method can achieve higher prediction accuracy compared with current state-of-the-art approaches.

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Arabic dialect sentiment analysis with ZERO effort. \\ Case study: Algerian dialect

INTELIGENCIA ARTIFICIAL ◽

10.4114/intartif.vol23iss65pp124-135 ◽

2020 ◽

Vol 23 (65) ◽

pp. 124-135

Author(s):

Imane Guellil ◽

Marcelo Mendoza ◽

Faical Azouaou

Keyword(s):

Sentiment Analysis ◽

Short Term Memory ◽

State Of The Art ◽

Short Term ◽

Term Memory ◽

Ongoing Work ◽

Long Short Term Memory ◽

Large Corpus ◽

Unique Condition

This paper presents an analytic study showing that it is entirely possible to analyze the sentiment of an Arabic dialect without constructing any resources. The idea of this work is to use the resources dedicated to a given dialect \textit{X} for analyzing the sentiment of another dialect \textit{Y}. The unique condition is to have \textit{X} and \textit{Y} in the same category of dialects. We apply this idea on Algerian dialect, which is a Maghrebi Arabic dialect that suffers from limited available tools and other handling resources required for automatic sentiment analysis. To do this analysis, we rely on Maghrebi dialect resources and two manually annotated sentiment corpus for respectively Tunisian and Moroccan dialect. We also use a large corpus for Maghrebi dialect. We use a state-of-the-art system and propose a new deep learning architecture for automatically classify the sentiment of Arabic dialect (Algerian dialect). Experimental results show that F1-score is up to 83% and it is achieved by Multilayer Perceptron (MLP) with Tunisian corpus and with Long short-term memory (LSTM) with the combination of Tunisian and Moroccan. An improvement of 15% compared to its closest competitor was observed through this study. Ongoing work is aimed at manually constructing an annotated sentiment corpus for Algerian dialect and comparing the results

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Optimal and Secure Electricity Market Framework for Market Operation of Multi-Microgrid Systems

10.25148/etd.fidc009166 ◽

2020 ◽

Author(s):

Mohammad Mahmoudian Esfahani

Keyword(s):

Electricity Market ◽

Market Operation ◽

Market Framework

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The energy trilemma in the policy design of the electricity market

DYNA ◽

10.15446/dyna.v82n194.48595 ◽

2015 ◽

Vol 82 (194) ◽

pp. 160-169 ◽

Cited By ~ 1

Author(s):

Carlos Jaime Franco Cardona ◽

Mónica Castañeda Riascos ◽

Alejandro Valencia Arias ◽

Jonathan Bermúdez Hernández

Keyword(s):

Energy Policy ◽

Electricity Markets ◽

Electricity Market ◽

Policy Design ◽

State Of The Art ◽

Economic Sustainability ◽

Security Of Supply ◽

Section 8 ◽

Market Competitiveness ◽

Environmental Goals

The energy "Trilemma" seeks to develop an electricity market which simultaneously ensures environmental quality, security of supply, and economic sustainability. The objective of this paper is to present the "Trilemma" energy as the latest trend in the design of energy policy. For this, a theoretical framework is presented in sections 2 and 3, in section 4 and 5 the importance of security of supply and economic sustainability are discussed, respectively. In section 6 the energy "trilemma" is presented, in section 7 a brief state of the art is showed. Finally in section 8, it is approached three different electricity markets. It is concluded that the regulator has passed in recent years from encouraging a liberalized market scheme, to promote a scheme based on intervention through policies that affect the market competitiveness but allow achieving its environmental goals.

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Learning Long- and Short-Term User Literal-Preference with Multimodal Hierarchical Transformer Network for Personalized Image Caption

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i05.6503 ◽

2020 ◽

Vol 34 (05) ◽

pp. 9571-9578 ◽

Cited By ~ 1

Author(s):

Wei Zhang ◽

Yue Ying ◽

Pan Lu ◽

Hongyuan Zha

Keyword(s):

State Of The Art ◽

Natural Extension ◽

Target Image ◽

Short Term ◽

Image Representations ◽

High Level ◽

Image Descriptions ◽

Shed Light ◽

Image Caption

Personalized image caption, a natural extension of the standard image caption task, requires to generate brief image descriptions tailored for users' writing style and traits, and is more practical to meet users' real demands. Only a few recent studies shed light on this crucial task and learn static user representations to capture their long-term literal-preference. However, it is insufficient to achieve satisfactory performance due to the intrinsic existence of not only long-term user literal-preference, but also short-term literal-preference which is associated with users' recent states. To bridge this gap, we develop a novel multimodal hierarchical transformer network (MHTN) for personalized image caption in this paper. It learns short-term user literal-preference based on users' recent captions through a short-term user encoder at the low level. And at the high level, the multimodal encoder integrates target image representations with short-term literal-preference, as well as long-term literal-preference learned from user IDs. These two encoders enjoy the advantages of the powerful transformer networks. Extensive experiments on two real datasets show the effectiveness of considering two types of user literal-preference simultaneously and better performance over the state-of-the-art models.

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Learning from Interventions Using Hierarchical Policies for Safe Learning

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i06.6602 ◽

2020 ◽

Vol 34 (06) ◽

pp. 10352-10360

Author(s):

Jing Bi ◽

Vikas Dhiman ◽

Tianyou Xiao ◽

Chenliang Xu

Keyword(s):

Reaction Time ◽

State Of The Art ◽

The State ◽

Policy Framework ◽

Asymptotic Performance ◽

Short Term ◽

Learning From Demonstrations ◽

Hierarchical Levels ◽

Long Term Behavior

Learning from Demonstrations (LfD) via Behavior Cloning (BC) works well on multiple complex tasks. However, a limitation of the typical LfD approach is that it requires expert demonstrations for all scenarios, including those in which the algorithm is already well-trained. The recently proposed Learning from Interventions (LfI) overcomes this limitation by using an expert overseer. The expert overseer only intervenes when it suspects that an unsafe action is about to be taken. Although LfI significantly improves over LfD, the state-of-the-art LfI fails to account for delay caused by the expert's reaction time and only learns short-term behavior. We address these limitations by 1) interpolating the expert's interventions back in time, and 2) by splitting the policy into two hierarchical levels, one that generates sub-goals for the future and another that generates actions to reach those desired sub-goals. This sub-goal prediction forces the algorithm to learn long-term behavior while also being robust to the expert's reaction time. Our experiments show that LfI using sub-goals in a hierarchical policy framework trains faster and achieves better asymptotic performance than typical LfD.

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