District heating temperature control algorithm based on short term weather forecast and consumption predictions

Papakonstantinou, Nikolaos; Savolainen, Jouni; Koistinen, Jarmo; Aikala, Antti; Vyatkin, Valeriy

doi:10.1109/etfa.2016.7733748

Cited by 7 publications

(4 citation statements)

References 19 publications

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“…Papakonstantinou et al [79] developed a controller that assesses the lowest temperature it could send to a district heating network, through prediction of the heat demand and calculation of the time delays in the network. More specifically, by grouping customers with equal temperature propagation delays, the required supply temperature to meet the demand in each of these groups can be determined.…”

Section: Other Central Control Approachesmentioning

confidence: 99%

Controlling district heating and cooling networks to unlock flexibility: A review

Vandermeulen

Heijde

Helsen

2018

Energy

259

132

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Flexibility in district heating and cooling systems (thermal networks in general) is an important means to cope with the intermittent generation of heat and electricity as the share of renewable energy sources (RES) increases. An important source of flexibility is the thermal energy storage present in district heating and cooling networks, found in the thermal inertia of buildings, storage units and the network itself. To unlock this flexibility and to use it effectively and efficiently, a suitable control strategy is required. In this context, this paper presents a possible definition of flexibility and its sources in a thermal network. It reviews techniques to quantify flexibility and shows the need for a more advanced control strategy; moreover, it discusses the challenges involved in developing such a control strategy. Also, the literature on advanced control in thermal networks is reviewed, by making a distinction between central, distributed and hybrid control. Finally, possible future research topics are identified based on the findings.

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Section: Other Central Control Approachesmentioning

confidence: 99%

Controlling district heating and cooling networks to unlock flexibility: A review

Vandermeulen

Heijde

Helsen

2018

Energy

259

132

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“…Untuk mengurangi ketergantungan komponen elektronika seperti sensor suhu, peneliti mengusulkan sebuah sistem untuk menggantikan peran sensor tanpa mengurangi atau mengganggu kinerja dari smart system. Sebuah sistem yang mampu meramalkan kondisi suhu lingkungan dapat menjadi solusi untuk masalah ini [6].…”

Section: Pendahuluanunclassified

Model Decision Tree Forecasting Berbasis DHT22 pada Smart Hydroponic Microgreen

Hadi,

Yasi,

Prasetyo

2024

J. Telecomm. Electr. Control Eng.

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Sensor DHT22 diharuskan aktif selama 4 hari penuh, jika dilihat dari waktu rata-rata budidaya microgreen kacang hijau menggunakan smart hydroponic. Penggunaan sensor DHT22 dalam jangka waktu tertentu dapat mempengaruhi kestabilan jangka panjang dalam hal pembacaan suhu. Jika pembacaan DHT22 mulai tidak akurat, smart system tidak mampu bekerja maksimal. Peneliti mengusulkan sebuah sistem untuk menggantikan peran sensor tanpa mengurangi atau mengganggu kinerja dari smart system untuk mengurangi ketergantungan komponen elektronika seperti sensor suhu. Seiring kemajuan teknologi, terdapat salah satu model machine learning yang dapat diterapkan untuk menggantikan peran sensor yaitu prediksi suhu melalui forecasting. Algoritma forecasting yang digunakan adalah decision tree. Algoritma ini dipilih karena mampu memprediksi data hanya dengan satu jenis input data berupa waktu dengan proses pelatihan yang cukup cepat. Data latih dihasilkan dari perekaman data selama 4 hari pada smart hydroponic microgreen kacang hijau. Model akan dibuat menggunakan grid search cross validation dan feature scaling. Hasil penelitian menunjukkan channel 80 layak dipilih menjadi model prediksi. Prediksi suhu model decision tree forecasting menghasilkan nilai R-squared sebesar 0,870955 dan mean square error (MSE) sebesar 0,074171. Kedua nilai tersebut menunjukkan bahwa model cukup kuat dalam memprediksi suhu dan layak untuk diterapkan dalam memantau suhu smart hydroponic microgreen kacang hijau.

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“…Nikolaos et al proposed an algorithm that aims to provide more heat energy to the difficult consumers when they need it the most [41]. The required input information are the short term weather forecast, and the supply hot water temperature propagation delays of the district heating grid as a function of the grid load level and consumption profiles.…”

Section: Current Conditionsmentioning

confidence: 99%

Recent Trends in Load Forecasting Technology for the Operation Optimization of Distributed Energy System

Tian

Yan

et al. 2017

Energies

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Abstract:The introduction of renewable resources into the distributed energy system has challenged the operation optimization of the distributed energy system. Integration of new technologies and diversified characteristics on the demand side has exerted a great influence on the distributed energy system. In this paper, by way of literature review, first, the topological structure and the mathematical expression of the distributed energy system were summarized, and the trend of enrichment and diversification and the new characteristics of the system were evaluated. Then, the load forecasting technology was reviewed and analyzed from two aspects, fundamental research and application research. Research methods of the distributed energy system under the new trend of energies were discussed, and the boundaries of the broadened distributed energy technology were explored.

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District heating temperature control algorithm based on short term weather forecast and consumption predictions

Cited by 7 publications

References 19 publications

Controlling district heating and cooling networks to unlock flexibility: A review

Controlling district heating and cooling networks to unlock flexibility: A review

Model Decision Tree Forecasting Berbasis DHT22 pada Smart Hydroponic Microgreen

Recent Trends in Load Forecasting Technology for the Operation Optimization of Distributed Energy System

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