Recent Development in Electricity Price Forecasting Based on Computational Intelligence Techniques in Deregulated Power Market

The development of artificial intelligence (AI) based techniques for electricity price forecasting (EPF) provides essential information to electricity market participants and managers because of its greater handling capability of complex input and output relationships. Therefore, this research investigates and analyzes the performance of different optimization methods in the training phase of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) for the accuracy enhancement of EPF. In this work, a multi-objective optimization-based feature selection technique with the capability of eliminating non-linear and interacting features is implemented to create an efficient day-ahead price forecasting. In the beginning, the multi-objective binary backtracking search algorithm (MOBBSA)-based feature selection technique is used to examine various combinations of input variables to choose the suitable feature subsets, which minimizes, simultaneously, both the number of features and the estimation error. In the later phase, the selected features are transferred into the machine learning-based techniques to map the input variables to the output in order to forecast the electricity price. Furthermore, to increase the forecasting accuracy, a backtracking search algorithm (BSA) is applied as an efficient evolutionary search algorithm in the learning procedure of the ANFIS approach. The performance of the forecasting methods for the Queensland power market in the year 2018, which is well-known as the most competitive market in the world, is investigated and compared to show the superiority of the proposed methods over other selected methods.© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Prediksi Ketersediaan Benih Padi (Oryza Sativa.L) Di Provinsi Maluku Menggunakan Metode Adaptive Neuro-Fuzzy Inference System (Anfis) - Prediction Of The Rice Seeds Availability (Oryza Sativa.L) In The Province Of Maluku By Using Adaptive Neuro Fuzzy Inferensi System (ANFIS) Method

Sistem perbenihan yang tangguh (produktif, efisien, berdaya saing dan berkelanjutan) sangat diperlukan untuk mendukung upaya peningkatan produksi benih dan mutu produk pertanian. Penelitian ini bertujuan untuk memprediksi ketersediaan benih padi yang ada di Provinsi Maluku.dari data yang tersedia yaitu variabel Sisa periode lalu, tambah stok, jumlah stok, tersalur dan sisa stok. Variabel tersebut kemudian diolah membentuk tiga selang linguistik yaitu rendah, sedang dan tinggi kemudian diolah menggunakan Adaptive Neuro Fuzzy (Anfis) yang membentuk lima Member Function input dan satu output. Hasil dari proses training anfis menunjukan bahwa pada iterasi kedua telah mencapai eror konstan sebesar 0,057018 dan nilai eror rata-rata 0,0015364. Perhitungan tersbut kemudian dibandingkan dengan data aktual yang ada untuk menghitung MAPE dan RMSE diperoleh hasil MAPE sebesar 5.316884, dan RMSE sebesar 28.28583 dengan demikian sistim yang dipergunakan cukup valid dan dapat dipergunakan untuk menentukan prediksi ketersediaan padi. ======================================================================================================================== A formidable seeding process (productive, efficient, competitive and continuous seeding process) is highly necessary to support the effort to increase seeds production quantity and the quality of agricultural product. Our propose is done to predict the availability of rice seeds in Maluku by using Adaptive Neuro Fuzzy (Anfis)which forming five Members Function input and one output. from the availability data, which variable of the last period, additional stock, total stock, is channeled and the last stock. The variable then process to be 3 pipes of linguistic which are low, medium, and high by using the Adaptive Neuro Fuzzy (Anfis) which make five input member functions and one output. The result of this anfis training process shows that the constant error value of 0,057018 and the average error value of 0,0015364have been reached on the second iteration. This calculation, which used artificial intelligence, is then compared to the actual data to calculate MAPE and RMSE which are gained from the MAPE result of 5.316884, and RMSE as much as 28.2858; thus, the system used here is valid and can be used to determine the prediction of rice seeds availability

Hibrit algoritma kullanarak elektrik enerji tüketim modelinin oluşturulması ve kestirimi : Uganda örneği

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Uzun vadeli elektrik tüketimi tahmini karar vericiler tarafından sistem genişletme planlaması konusunda karar vermek için kullanılır. Geçtiğimiz on yıl boyunca, elektrik tüketim tahminleri üzerine yapılan araştırmaların nokta tahminleri olarak sonuçları rapor edilmiştir. Özellikle uzun vadeli tahminler için nokta tahminleri çok fazla ilgi çekici değildir. Çünkü bunun sistem genişletme ile ilgili finansal riskinin, talep değişkenliğinin ve tahmin belirsizliğinin tahmin edilmesi için kullanılması güçtür. Bu çalışmada ilk olarak, Uganda'nın net elektrik tüketimini modellemek için, tahmin modellerinde nüfusu, gayri safi yurtiçi hasılayı, abone sayısını ve ortalama elektrik fiyatını değişken olarak gözönüne almak suretiyle üstel, karesel ve Adaptif sinirsel bulanık çıkarım sistemi (ANFIS) formları kullanılmıştır. Parçacık Sürüsü Optimizasyonu (PSO) ve Yapay Arı Kolonosi (YAK) algoritmalarına dayalı bir hibrit algoritma kullanılarak üstel ve karesel tahmin modellerinin parametreleri optimize edilmiştir. ANFIS modelinin parametreleri ise, PSO ve Genetik Algoritma (GA) kullanılarak optimize edilmiştir. İkinci olarak, %90 anlamlılık düzeyli alt ve üst hata sınırlarını elde etmek için basit doğrusal regresyonu kullanarak tahmin kalıntıları modellenmiştir. Uganda'nın 2040 yılına kadarki net elektrik tüketimine ilişkin tahmin aralıklarını oluşturmak için alt ve üst hata sınırları kullanılmıştır. Son olarak, birleştirilmiş öngörme modeli elde etmek için bu dört yönteme ilişkin dört model de birleştirilmiştir. Birleştirilmiş tahminlere göre, 2040 yılında Uganda'nın elektrik tüketim tahmininin, yıllık ortalama %11,75 - %10,64'lük bir artışa işaretle [41,296 42,133] GWh arasında olacağı tahmin edilmiştirLong term electricity consumption forecasting is used by decision makers to make decisions regarding system expansion planning. Over the past decade, research on electricity consumption forecasting has reported results as point forecasts. Specifically for long-term forecasting, point forecasts are of little interest because it is hard to use them to assess the financial risk associated with system expansion versus demand variability and forecasting uncertainty. In this study, firstly we use power, quadratic and Adaptive Neuro Fuzzy Inference System (ANFIS) forms to model Uganda's net electricity consumption using population, gross domestic product, number of subscribers and average electricity price as variables in the forecasting models. We optimize the parameters of power and quadrtaic forecasting models using a hybrid algorithm based on particle swarm optimization (PSO) and artificial bee colony (ABC) algorithms. The parameters of ANFIS model are optmized using particle swarm optimization and genetic algorithm. Secondly we model the forecast residuals using simple linear regression to obtain 90% significance level lower and upper error bounds. The lower and upper error bounds were used to construct predication intervals for Uganda's net electricity consumption up to year 2040. Finally we combine all the four models from the two methods to get a combined forecasting model. According to the combined forecast, in year 2040 Uganda's electricity consumption will be between [41,296 42,133] GWh indicating an annual average increase of 11.75%-10.64

Optimisation and Operation of Residential Micro Combined Heat and Power (μCHP) Systems

In response to growing concerns regarding global warming and climate change, reduction of CO2 emissions becomes a priority for many countries, especially the developed ones such as the UK. Residential applications are considered among the most important areas for substantial reduction of CO2 emissions because they represent a major part of the total consumed energy in those countries. For instance, in the UK, residential applications are currently accountable for about 150 Mt CO2 emissions, which represents approximately 25% of the whole CO2 emissions [1-2]. In order to achieve a significant CO2 reduction, many strategies must be adopted in the policy of these countries. One of these strategies is to introduce micro combined heat and power (μCHP) systems into residential energy systems, since they offer several advantages over traditional systems. A significant amount of research has been carried out in this field; however, in terms of integrating such systems into residential energy systems, significant work is yet to be conducted. This is because of the complexity of these systems and their interdependency on many uncertain variables, energy demand of a house is a case in point. In order to achieve such integration, this research focuses on the optimisation and operation of μCHP systems in residential energy systems as essential steps towards integration of these systems, so it deals with the optimisation and operation of a μCHP system within a building taking into account that the system is grid-connected in order to export or import electricity in certain cases. A comprehensive review that summarises key points that outline the trend of previous research in this field has been carried out. The reviewed areas include: technologies used as residential μCHP units, modelling of the μCHP systems, sizing of μCHP systems and operation strategies used for such systems. To further this, a generic model for sizing of μCHP system’s components to meet different residential application has been developed by the author. Two different online operation strategies of residential μCHP systems, namely: an online linear programming optimiser (LPO) and a real time fuzzy logic operation strategy (FLOS) have been developed. The performance of the novel online operation strategies, in terms of their ability to reduce operation costs, has been evaluated. Both the LPO and the FLOS were found to have their advantages when compared with the traditional operation strategies of μCHP systems in terms of operation costs and CO2 emissions. This research should therefore be useful in informing design and operation decisions during developing and implementing μCHP technologies in residential applications, especially single dwellings