Energy and Environment Implications of Long-Term Power Development Involving Renewable Energy: a Case of Timor Island, Indonesia

This paper presents a long-term electricity energy supply-demand model of Timor Island, Indonesia. Implications of projected demand growth within the observed area towards the available supply as well as the amount of CO2 emission is taken into account. As its main objective is to review and to present initial comparison of the long-term electricity planning prepared by the utility, the analysis is carried out using the bottom-up energy system model. Unlike the common electricity long-term demand projection that is usually constructed based on the factors related to the electricity growth, the model is developed based on the simple projection considering historical electricity demand users. According to the analysis, the planned power plants would not able to meet the electricity demand in the case of high growth demand scenario. The variation of CO2 emission that is obtained from the considered scenarios is also shown

TÜRKİYE'DE ENERJİ TALEBİNİ TAHMİN ETMEK İÇİN DOĞRUSAL FORM KULLANARAK GSA (YERÇEKİMİ ARAMA ALGORİTMASI) VE IWO (YABANİ OT OPTİMİZASYON ALGORİTMASI) TEKNİKLERİNİN UYGULANMASI

Bu çalışma, Türkiye'deki ekonomik göstergelere dayalı enerji talep tahmini ile ilgilidir. Enerji talebini tahmin etmek için Yerçekimi Arama Algoritması (GSA) ve Yabani Ot Algoritması (IWO) tekniklerine dayanan iki farklı model önerilmektedir. GSA yöntemi, Newton’un hareket ve yerçekimi kanunlarından esinlenerek geliştirilmiş sezgisel optimizasyon algoritmasıdır. IWO algoritması ise doğadaki yabani otların istilacı karakterlerinden esinlenen, evrimsel bir optimizasyon algoritmasıdır. GSA ve IWO yöntemlerine dayalı enerji talep modelleri, gayri safi yurtiçi hâsıla (GSYİH), nüfus, ithalat ve ihracat verilerini giriş parametresi şeklinde kullanan bir model olarak önerilmektedir. Önerilen yöntemler doğrusal regresyon modeli kullanılarak geliştirilmiştir. Türkiye’nin gelecekteki enerji talebi ise üç farklı senaryo altında tahmin edilmektedir. Önerilen tahmin modellerinden elde edilen deneysel sonuçlar karşılaştırmalı olarak verilmiştir. 1979 ve 2005 yılları arasındaki veriler kullanılarak gerçekleştirilen tahmin modelinde IWO literatürdeki diğer yöntemlerle de kıyaslanmış ve IWO yöntemi en yüksek performansı verdiği görülmüştür. 1979 ve 2011 yılları arasındaki tüm veri seti kullanılarak gerçekleştirilen tahmin modelinde ise GSA, IWO yöntemiyle karşılaştırılmış ve GSA daha iyi bir performans elde etmiştir

An applıcatıon based on the tunıcate swarm algorıthm for predıctıon the energy demand of Turkey

Enerji talebinin tahmini, her ülke için oldukça önemli bir konudur. Çünkü bir ülkenin ekonomisi, enerji talebinden doğrudan etkilenmektedir. Bu nedenle, yapılan bu çalışmada, Türkiye’nin gelecekteki enerji talebini tahmin etmek için tulumlular sürü algoritması (TSA) tabanlı doğrusal bir yaklaşım önerilmektedir. Doğrusal modelin elde edilmesi aşamasında, bir ülkenin gayri safi yurtiçi hasıla, nüfus, ithalat ve ihracat verileri modelin giriş parametreleri olarak alınmıştır. Daha sonra bu parametrelerin optimum ağırlık katsayılarını bulmak amacıyla optimizasyon problemlerinin çözümü için önerilmiş olan popülasyon tabanlı TSA algoritması kullanılmıştır. Önerilen modelin eğitim ve test aşaması için Türkiye’nin 1979-2011 arasındaki yıllara ait olan veri seti kullanılmıştır. Doğrusal model oluşturulduktan sonra, Türkiye’nin 2012’den 2030’a kadar olacak şekilde yaklaşık olarak 20 yıllık bir süre için enerji talebi, üç farklı muhtemel senaryo için tahmin edilmiştir. Daha sonra ise önerilen model ile elde edilen deneysel sonuçlar, Türkiye’nin enerji talebini için literatürde önerilmiş olan diğer algoritmaların elde ettiği deneysel sonuçlar ile karşılaştırılmıştır. Deneysel sonuçlar ve karşılaştırmalar değerlendirildiğinde, bu çalışma kapsamında önerilmiş olan TSA tabanlı model, Türkiye’nin geleceğe dönük enerji talebini tahmin etmek için rekabetçi ve başarılı sonuçlar elde etmiştir

An Improved Artificial Colony Algorithm Model for Forecasting Chinese Electricity Consumption and Analyzing Effect Mechanism

Electricity consumption forecast is perceived to be a growing hot topic in such a situation that China’s economy has entered a period of new normal and the demand of electric power has slowed down. Therefore, exploring Chinese electricity consumption influence mechanism and forecasting electricity consumption are crucial to formulate electrical energy plan scientifically and guarantee the sustainable economic and social development. Research has identified medium and long term electricity consumption forecast as a difficult study influenced by various factors. This paper proposed an improved Artificial Bee Colony (ABC) algorithm which combined with multivariate linear regression (MLR) for exploring the influencing mechanism of various factors on Chinese electricity consumption and forecasting electricity consumption in the future. The results indicated that the improved ABC algorithm in view of the various factors is superior to traditional models just considering unilateralism in accuracy and persuasion. The overall findings cast light on this model which provides a new scientific and effective way to forecast the medium and long term electricity consumption

Particle swarm grammatical evolution for energy demand estimation

[EN] Grammatical Swarm is a search and optimization algorithm that belongs to the more general Grammatical Evolution family, which works with a set of solutions called individuals or particles. It uses the Particle Swarm Optimization algorithm as the search engine in the evolution of solutions. In this paper, we present a Grammatical Swarm algorithm for total energy demand estimation in a country from macroeconomic variables. Each particle in the Grammatical Swarm encodes a different model for energy demand estimation, which will be decoded by a predefined grammar. The parameters of the model are also optimized by the proposed algorithm, in such a way that the model is adjusted to a training set of real energy demand data, selecting the more appropriate variables to appear in the model. We analyze the performance of the Grammatical Swarm evolution in two real problems of one-year ahead energy demand estimation in Spain and France. The proposal is compared with previous approaches with competitive results.Spanish Ministerial Commission of Science and Technology (MICYT), Grant/Award Number: TIN2017-85887-C2-2-P; Ministerio de Ciencia, Innovacion y Universidades, Grant/Award Number: PGC2018-095322-B-C22 and RTI2018-095180-B-I00; Comunidad de Madrid y Fondos Estructurales de la Union Europea, Grant/Award Number: S2018/TCS-4566 and Y2018/NMT-4668; GenObIA-CM, Grant/Award Number: S2017/BMD-3773; Ministerio de Economia, Industria y Competitividad, Grant/Award Number: MTM2017-89664-PMartínez-Rodríguez, D.; Colmenar, JM.; Hidalgo, JI.; Villanueva Micó, RJ.; Salcedo-Sanz, S. (2020). Particle swarm grammatical evolution for energy demand estimation. Energy Science & Engineering. 8(4):1068-1079. https://doi.org/10.1002/ese3.568S1068107984Safarzyńska, K., & van den Bergh, J. C. J. M. (2017). Integrated crisis-energy policy: Macro-evolutionary modelling of technology, finance and energy interactions. 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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

Assessment and modelling of energy use and indoor environment towards conservation in historical art gallery buildings

This PhD study presents a set of non-invasive methods developed to assess and model the indoor environmental conditions and the building energy use in the National Galleries of Scotland (NGS). This was to meet three intentions, firstly, to provide a detailed and efficient guidance to the facility managers of such building type on the building’s indoor environmental performance with respect to artwork conservation standards, and energy performance with respect to benchmarks from official standards such as CIBSE. Secondly, to provide good practice guidance on latent energy investment towards maintaining indoor moisture conditions relative to conservation specifications. Motivation behind this moisture control was found to be the parameter which is the most critical to artwork conservation, and previous studies revealing the significant amount of energy costs associated to meet the demands of maintaining the adequate indoor moisture specifications. And thirdly, to provide a robust tool which can mimic the complex, non linear building system and provide forecasting with high speed and accuracy. This model also enables the building management to test various optimisation options, while attempting to reduce energy consumption in the building while adhering to artwork conservation standards. The assessment methods were developed following a large-scale refurbishment event in the NGS, and involved a post-renovation impact study. The latent energy investment was analysed with the help of a new weather feature variable, developed as a part of this study. This was named as ‘Humidity-Day’ (HD) concept, analogous to the Degree Day concept. Artificial Intelligence (AI) was employed to model the complex NGS building system and predict indoor temperature, RH and building energy consumption – Gas and Electricity. This directly catered to the need to test optimisation strategies to cut down energy costs without jeopardising the healthy conditions of delicate artworks housed in the building. The positive effects of refurbishment in the NGS were highlighted by performance indicators. An overall indoor environment improvement of 16% was observed, out of which maintenance of indoor RH improved by 4% and the same for temperature by 12%. Winters experienced the maximum overall indoor environmental improvement of 59%. The indoor stability assessed by newly developed fluctuation parameters for both hourly and daily cases highlighted that the NGS experienced stable indoor temperature and RH, especially after the refurbishment. In addition to the benefits to indoor environment, the refurbishment regime brought a cut-down in NGS gas consumption by 27%. The Humidity Day Concept was developed and applied as a global climatic indicator focusing on moisture extremes relative to conservation specifications. Next, the HD based humidification estimates were employed as a good practice indicator and the humidification action of the NGS in the year 2015 was checked for over-consumption periods in a year. It was observed that 33% of the time, there was overconsumption related to humidification, especially during the winter months. Maximum overconsumption was experienced during October and November, where the NGS humidifier load exceeded the good practice mark by up to two times. The system identification model of the NGS was tested with excellent accuracies of up to 99% correlation between predicted results and the actual recorded data. It is also concluded that ANNs are able to work with limited amount of building systems data (real data) readily available from the building management. The study further reinstates that the ANN based SI model can prove to be an ideal platform to investigate various optimisation strategies of the building operation in future, especially in the case of restrictive traditional building types where any retrofit solution needs a strong scientific backing of guaranteed success before practical implementation. In future, work will be done to further strengthen the Humidity Day concept and test the case of dehumidification by further working on some of the assumptions. Furthermore, sub-metering at the NGS will provide accurate data to help validate the findings, especially, the energy consumed by chillers and humidifiers during the winter months will give a required justification for the dehumidification figures obtained

Indoor air Quality and Its Effects on Health in Urban Houses of Indonesia: A case study of Surabaya

There is a possibility that the sick building syndrome has already spread widely among the newly constructed apartments in major cities of Indonesia. This study investigates the current conditions of indoor air quality, focusing especially on formaldehyde and TVOC, and their effects on health among occupants in the urban houses located in the city of Surabaya. A total of 471 respondents were interviewed and 82 rooms were measured from September 2017 to January 2018. The results indicated that around 50% of the respondents in the apartments showed some degrees of chemical sensitivity risk. More than 60% of the measured formaldehyde levels in the apartments exceeded the WHO standard, 0.08 ppm. The respondents living in rooms with higher mean formaldehyde values tended to have higher multiple chemical sensitivity risk scores. KEYWORDS: Indoor air quality, Sick building syndrome, QEESI, Formaldehyde, Developing countrie