A Hybrid Clustering and Classification Technique for Forecasting Short-Term Energy Consumption

Electrical energy distributor companies in Iran have to announce their energy demand at least three 3-day ahead of the market opening. Therefore, an accurate load estimation is highly crucial. This research invoked methodology based on CRISP data mining and used SVM, ANN, and CBA-ANN-SVM (a novel hybrid model of clustering with both widely used ANN and SVM) to predict short-term electrical energy demand of Bandarabbas. In previous studies, researchers introduced few effective parameters with no reasonable error about Bandarabbas power consumption. In this research we tried to recognize all efficient parameters and with the use of CBA-ANN-SVM model, the rate of error has been minimized. After consulting with experts in the field of power consumption and plotting daily power consumption for each week, this research showed that official holidays and weekends have impact on the power consumption. When the weather gets warmer, the consumption of electrical energy increases due to turning on electrical air conditioner. Also, con-sumption patterns in warm and cold months are different. Analyzing power consumption of the same month for different years had shown high similarity in power consumption patterns. Factors with high impact on power consumption were identified and statistical methods were utilized to prove their impacts. Using SVM, ANN and CBA-ANN-SVM, the model was built. Sine the proposed method (CBA-ANN-SVM) has low MAPE 5 1.474 (4 clusters) and MAPE 5 1.297 (3 clusters) in comparison with SVM (MAPE 5 2.015) and ANN (MAPE 5 1.790), this model was selected as the final model. The final model has the benefits from both models and the benefits of clustering. Clustering algorithm with discovering data structure, divides data into several clusters based on similarities and differences between them. Because data inside each cluster are more similar than entire data, modeling in each cluster will present better results. For future research, we suggest using fuzzy methods and genetic algorithm or a hybrid of both to forecast each cluster. It is also possible to use fuzzy methods or genetic algorithms or a hybrid of both without using clustering. It is issued that such models will produce better and more accurate results. This paper presents a hybrid approach to predict the electric energy usage of weather-sensitive loads. The presented methodutilizes the clustering paradigm along with ANN and SVMapproaches for accurate short-term prediction of electric energyusage, using weather data. Since the methodology beinginvoked in this research is based on CRISP data mining, datapreparation has received a gr eat deal of attention in thisresear ch. Once data pre-processing was done, the underlyingpattern of electric energy consumption was extracted by themeans of machine learning methods to precisely forecast short-term energy consumption. The proposed approach (CBA-ANN-SVM) was applied to real load data and resulting higher accu-racy comparing to the existing models. 2018 American Institute of Chemical Engineers Environ Prog, 2018 https://doi.org/10.1002/ep.1293

Short-term load forecasting of microgrid via hybrid support vector regression and long short-term memory algorithms

© 2020 by the authors. Short-Term Load Forecasting (STLF) is the most appropriate type of forecasting for both electricity consumers and generators. In this paper, STLF in a Microgrid (MG) is performed via the hybrid applications of machine learning. The proposed model is a modified Support Vector Regression (SVR) and Long Short-Term Memory (LSTM) called SVR-LSTM. In order to forecast the load, the proposed method is applied to the data related to a rural MG in Africa. Factors influencing the MG load, such as various household types and commercial entities, are selected as input variables and load profiles as target variables. Identifying the behavioral patterns of input variables as well as modeling their behavior in short-term periods of time are the major capabilities of the hybrid SVR-LSTM model. To present the efficiency of the suggested method, the conventional SVR and LSTM models are also applied to the used data. The results of the load forecasts by each network are evaluated using various statistical performance metrics. The obtained results show that the SVR-LSTM model with the highest correlation coefficient, i.e., 0.9901, is able to provide better results than SVR and LSTM, which have the values of 0.9770 and 0.9809, respectively. Finally, the results are compared with the results of other studies in this field, which continued to emphasize the superiority of the SVR-LSTM model

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

Optimal Control of Microgrids in Different Operation Conditions

У дисертацији је дат концепт микро мрежа и описане постојеће методе у управљању и оптимизацији рада микро мрежа. Предложен је нови централизовани контролер микро мрежe заснован на технологији више-агентног система, који омогућава координацију три режима рада (повезани, острвски и хаваријски) и обезбеђује једноставну конфигурацију и комбинацију оптимизационих критеријума, уз уважавање широког скупа ограничења. Предложени модел примењен је на релевантни тест систем и резултати су приказани уз одговарајућу анализу резултата.U disertaciji je dat koncept mikro mreža i opisane postojeće metode u upravljanju i optimizaciji rada mikro mreža. Predložen je novi centralizovani kontroler mikro mreže zasnovan na tehnologiji više-agentnog sistema, koji omogućava koordinaciju tri režima rada (povezani, ostrvski i havarijski) i obezbeđuje jednostavnu konfiguraciju i kombinaciju optimizacionih kriterijuma, uz uvažavanje širokog skupa ograničenja. Predloženi model primenjen je na relevantni test sistem i rezultati su prikazani uz odgovarajuću analizu rezultata.Dissertation provides the microgrids concept and describes existing methods for control and optimization of microgrid operation. This paper proposes a novel, centralized, multi-agent-based, microgrid controller architecture, which provides the coordination of all three operation modes (grid-connected, island and emergency) and enables the easy configuration/combination of optimization goals that are subject to a given set of operational constraints. The simulation results are presented for a typical microgrid test example

New input identification and artificial intelligence based techniques for load prediction in commercial building

The accuracy of prediction models for electrical loads are important as the predicted result can affect processes related to energy management such as maintenance planning, decision-making processes, as well as cost and energy savings. The studies on improving load prediction accuracy using Least Squares Support Vector Machine (LSSVM) are widely carried out by optimizing the LSSVM hyper-parameter which includes the Kernel parameter and the regularization parameter. However, studies on the effects of input data determination for the LSSVM have not widely tested by researchers. This research developed an input selection technique using Modified Group Method of Data Handling (MGMDH) to improve the accuracy of buildings load forecasting. In addition, a new cascaded Group Method of Data Handing (GMDH) and LSSVM (GMDH-LSSVM) model is developed for electrical load prediction to improve the prediction accuracy of LSSVM model. To further improve the prediction model ability, a Modified GMDH has been cascaded to the LSSVM model to enhance the accuracy of building electrical load prediction and reduce the complexity of GMDH model. The proposed models are compared with GMDH model, LSSVM model and Artificial Neural Network (ANN) model to observe the prediction performance. The performances of prediction for each tested models are evaluated using the Mean Absolute Percentage Error (MAPE). In this analysis, the proposed prediction model, gives 33.82% improvement of prediction accuracy as compared to LSSVM model. From this research, it can be concluded that cascading the models can improve the prediction accuracy and the proposed models can be used to predict building electrical loads

スマートハウスにおける異なるエネルギーシステムの比較と経済的最適化に関する研究

In recent years, with the improvement of people\u27s living standards and the popularization of smart appliances, household power consumption shows an upward trend. Reasonable energy management combined with efficient and energy-saving equipment is an effective way to achieve energy conservation and emission reduction in the household sector. At present, the research and promotion of smart house in Japan are gradually increasing, and the government has also implemented relevant incentive policies. Based on the characteristics and advantages of smart house, this study analyzes and compares the economy and environment of different energy systems in smart house, and optimizes the economy from the three levels of users, equipment, and power market. It is hoped that this study can provide new ideas for the promotion of smart home and provide theoretical reference for the practical application of smart home.北九州市立大

Emerging Technologies for the Energy Systems of the Future

Energy systems are transiting from conventional energy systems to modernized and smart energy systems. This Special Issue covers new advances in the emerging technologies for modern energy systems from both technical and management perspectives. In modern energy systems, an integrated and systematic view of different energy systems, from local energy systems and islands to national and multi-national energy hubs, is important. From the customer perspective, a modern energy system is required to have more intelligent appliances and smart customer services. In addition, customers require the provision of more useful information and control options. Another challenge for the energy systems of the future is the increased penetration of renewable energy sources. Hence, new operation and planning tools are required for hosting renewable energy sources as much as possible

Optimal Control of Microgrids in Different Operation Conditions

У дисертацији је дат концепт микро мрежа и описане постојеће методе у управљању и оптимизацији рада микро мрежа. Предложен је нови централизовани контролер микро мрежe заснован на технологији више-агентног система, који омогућава координацију три режима рада (повезани, острвски и хаваријски) и обезбеђује једноставну конфигурацију и комбинацију оптимизационих критеријума, уз уважавање широког скупа ограничења. Предложени модел примењен је на релевантни тест систем и резултати су приказани уз одговарајућу анализу резултата.U disertaciji je dat koncept mikro mreža i opisane postojeće metode u upravljanju i optimizaciji rada mikro mreža. Predložen je novi centralizovani kontroler mikro mreže zasnovan na tehnologiji više-agentnog sistema, koji omogućava koordinaciju tri režima rada (povezani, ostrvski i havarijski) i obezbeđuje jednostavnu konfiguraciju i kombinaciju optimizacionih kriterijuma, uz uvažavanje širokog skupa ograničenja. Predloženi model primenjen je na relevantni test sistem i rezultati su prikazani uz odgovarajuću analizu rezultata.Dissertation provides the microgrids concept and describes existing methods for control and optimization of microgrid operation. This paper proposes a novel, centralized, multi-agent-based, microgrid controller architecture, which provides the coordination of all three operation modes (grid-connected, island and emergency) and enables the easy configuration/combination of optimization goals that are subject to a given set of operational constraints. The simulation results are presented for a typical microgrid test example