Utafiti huu unalenga kubadilisha tabia tata inayotokana na thermostatic valves za watumiaji zinazofunguka na kufunga kwa kujitegemea na bila kusawazishwa kulingana na indoor temperatures katika district heating system, kuwa central control decisions zinazoweza kutekelezwa kwenye heating substation. Watafiti waliunganisha indoor temperature, user temperature setting na valve-open duration data kutoka kila makazi, wakakokotoa average indoor temperature na area-weighted valve opening ratio; kwa kutumia indicators hizi mbili wakaunda “heating-state map” ya 3×3 inayogawa supply-demand condition ya system katika regions tisa.

Katika proposed framework, terminal user layer hutoa information kwa substation, lakini user valves hazidhibitiwi moja kwa moja kwa centralized command. Substation hupunguza secondary-network supply-water temperature na kubadilisha circulation-pump differential-pressure setting kulingana na operating region kwenye map. Hivyo inalenga kupunguza mass closure ya user valves inayosababishwa na oversupply ya heat kwenye high temperature, flow fluctuations na kuongezeka kwa pump energy.

Katika case study iliyotumia two-hour operating data kutoka full heating season ya university campus yenye buildings 48 mjini Tianjin, system hydraulic-resistance coefficient ilibainishwa kuwa 1,24 Pa/(m³/saa)²; differential-pressure settings za 244, 238 na 226 kPa zilikokotolewa kwa temperature regions tofauti. Utafiti unaripoti maximum electricity saving ya %10,04 na maximum heat saving ya %24. Hata hivyo, values hizi si fixed savings zinazopatikana katika all meteorological conditions; zinategemea outdoor-air temperature, degree ya initial heat oversupply na operating region.

Tatizo kuu ambalo utafiti unajaribu kutatua ni nini?

Katika district heating systems zinazotumika sana kaskazini mwa China, heating substation moja hutoa heat kwa buildings nyingi kupitia common secondary hot-water network. Katika conventional operation, circulation pump inaweza kuendeshwa kwa constant differential pressure na supply-water temperature ikawekwa kwa fixed weather-compensation curve inayotegemea outdoor temperature au operator experience.

Kwa matumizi ya smart thermostatic valves, watumiaji wanaweza kuweka target indoor temperatures zao, na valves hufunguka au kufunga ili kudhibiti room temperature kulingana na target hiyo. Ingawa structure hii huwapa watumiaji flexibility kubwa, huunda control problem mpya kwa central system:

• Kila valve hufanya kazi katika discrete states mbili pekee: open au closed.
• Maelfu ya possible user behaviors hazitokei kwa wakati mmoja, bali hubadilika asynchronously.
• Substation inaweza kufanya kazi kwa fixed au experience-based parameters bila kuona actual heat demand ya watumiaji.
• Maji yenye joto zaidi ya lazima yanapotumwa, rooms huoverheat na valves nyingi hufunga passively.
• Mass na irregular closure ya valves huongeza network resistance na kusababisha flow na pressure fluctuations.

Hali hii inaweza kuunda cycle ya “heat oversupply–valve closure–hydraulic imbalance–renewed excess energy use”. Swali kuu la utafiti ni jinsi ya kubadilisha independent user behaviors kuwa reliable na understandable demand indicators zinazoweza kutumika katika central substation.

Inatofautianaje na methods za awali?

Sehemu muhimu ya previous research hutumia opening percentage ya continuously adjustable valves kama central control variable. Hata hivyo, katika on-off valves, state ya “open” au “closed” iliyopimwa katika instant moja haitoshi yenyewe kuonyesha ni kiasi gani cha heat user alihitaji katika entire sampling interval.

Utafiti unajaribu kuziba pengo hili kwa kuunganisha types mbili za information:

• Indoor temperature: Inawakilisha actual thermal-comfort state aliyofikia user.
• Valve-open duration: Inawakilisha intensity ya heat demand katika given time interval.

Hivyo system haitathmini tu valves zinafanya nini, bali pia katika indoor-temperature conditions gani behavior hiyo inatokea. Kwa mfano, low valve-opening ratio inaweza kuashiria heat oversupply ikiwa room temperature ni high; ikiwa room temperature ni low, inaweza kuashiria users hawapo nyumbani au unusual operating condition.

Proposed hierarchical structure inafanyaje kazi?

Kielelezo 1 katika ukurasa wa 9 wa PDF kinaonyesha framework katika layers mbili kuu:

1. Terminal user layer

Information ifuatayo hupatikana kupitia radiators, smart thermostatic valves, room controllers na data collectors:

• User indoor temperature,
• User-defined target temperature,
• Valve open au closed state,
• Total open duration ndani ya sampling interval,
• User heated area.

Individual information hizi huhifadhiwa kwanza katika heating-state space na kisha kuaggregate kuwa average indoor temperature na valve opening ratio.

2. Heating substation layer

Katika substation, coordinated adjustments mbili hufanywa:

• Thermal optimization: Secondary supply-water temperature hupunguzwa au kurekebishwa kulingana na demand iliyotolewa kutoka user-state map.
• Hydraulic optimization: Circulation-pump differential pressure huwekwa upya kwa kutumia network resistance na region-specific target flow.

Information flow ni hierarchical na one-way. User layer hutoa demand information kwa substation; substation hubadilisha network temperature na pressure boundary conditions. System haibadilishi user target temperatures wala kulazimisha valves kufunguka au kufunga.

Control logic ya thermostatic valve ni nini?

Kielelezo 2 katika ukurasa wa 10 wa PDF kinaonyesha relation kati ya indoor temperature na valve state. Indoor temperature ikiwa chini ya user setting, valve hufunguka; setting value ikifikiwa, hufunga. User akiongeza target temperature, valve hufunguka tena; target ikipunguzwa, valve inaweza kubaki closed hadi room temperature ishuke hadi new lower threshold.

Behavior hii inaonyesha kwamba valve state haitegemei building heat loss pekee, bali pia user-modified temperature setting. Kwa hiyo watafiti walifafanua state vector ifuatayo kwa kila user:

\[ X_{n,i} = \begin{bmatrix} T_{n,i} & T_{n,\mathrm{set},i} & \tau_{n,i} \end{bmatrix} \]

• T_n,i: Indoor temperature ya user i katika sampling interval n, °C,
• T_n,set,i: User temperature setting, °C,
• τ_n,i: Total time valve ilibaki open katika same sampling interval, saa.

States za users wote huunganishwa katika matrix:

\[ X_n = \begin{bmatrix} T_{n,1} & T_{n,\mathrm{set},1} & \tau_{n,1} \\ T_{n,2} & T_{n,\mathrm{set},2} & \tau_{n,2} \\ \vdots & \vdots & \vdots \\ T_{n,K} & T_{n,\mathrm{set},K} & \tau_{n,K} \end{bmatrix} \]

Hapa K inawakilisha total number of users waliounganishwa na substation. PDF hairipoti total users au apartments katika case area.

Average indoor temperature inakokotolewaje?

Average indoor temperature ya users ndani ya substation scope inaelezwa kwa arithmetic mean ifuatayo:

\[ T_{n,\mathrm{aver}} = \frac{1}{K}\sum_{i=1}^{K}T_{n,i} \]

Mwishoni mwa printed Equation 3 katika PDF, expression “×100%” inaonekana. Kwa kuwa kubadilisha average temperature katika °C kuwa percentage haina physical meaning, expression hii imetathminiwa kama formatting au copying issue katika equation writing. Subsequent figures na calculations za utafiti hutumia T_n,aver katika °C.

Valve opening ratio inakokotolewaje?

Kwa kuwa apartment sizes zinaweza kutofautiana, valve-open durations hazikuaverage moja kwa moja. Badala yake, open duration ilipewa weight kwa heated area:

\[ \alpha = \frac{\sum_{i=1}^{K}\tau_i F_i} {\Delta\tau\sum_{i=1}^{K}F_i} \times 100\% \]

• α: Area-weighted valve opening ratio ndani ya substation scope, %,
• τ_i: Open duration ya valve ya user i, saa,
• F_i: Heated area ya user i, m²,
• Δτ: Sampling interval, saa.

Kwa mfano, valve ya apartment kubwa ikibaki open kwa saa mbili, itachangia zaidi kwenye total system heat-demand indicator kuliko apartment ndogo iliyobaki open kwa muda huohuo.

3×3 heating-state map iliundwaje?

Average indoor temperature iliwekwa kwenye horizontal axis na valve opening ratio kwenye vertical axis. First na third quartile values za variables zote mbili zilitumika kama boundaries na plane ikagawanywa katika regions tisa.

Katika general method, temperature columns ni:

• Column A: T_n,aver < U₁; low-temperature region,
• Column B: U₁ ≤ T_n,aver ≤ U₃; medium-temperature region,
• Column C: T_n,aver > U₃; high-temperature region.

Valve-opening-ratio rows ni:

• Row a: α < U₁; low valve-opening ratio na low-energy-efficiency region Ω_L,
• Row b: U₁ ≤ α ≤ U₃; medium opening ratio na normal dynamic operating region Ω_M,
• Row c: α > U₃; high opening ratio na high-efficiency region Ω_H.

Boundaries zilizopatikana kutoka case data ni:

Kielelezo 8 katika ukurasa wa 22 wa PDF kinaonyesha kwamba sehemu kubwa ya actual operating points zimekusanyika katika columns B na C. Valve opening ratio kupungua kwa ujumla kadiri indoor temperature inavyoongezeka kunaonyesha kwamba users hujibu overheating kwa kufunga valves zao.

Operating regions tisa zinamaanisha nini?

Lengo la map si kuhamisha operating points zote kwenye temperature column ileile. Lengo ni kuongeza valve opening ratio katika similar outdoor-air na indoor-temperature conditions, kupunguza unnecessary terminal throttling na kuhamisha operating point kwenye upper, more efficient row ya column ileile.

Operating data ilisafishwaje kutoka noise?

Kwa sababu district heating systems zina high thermal inertia, temperature changes ndani ya saa moja au mbili haziwezi kuwakilisha moja kwa moja control decision iliyotumika wakati huo. User behavior na sensor anomalies pia zinaweza kuunda scattered points katika raw data.

Watafiti walitumia “temperature-interval averaging” method inayogawa outdoor-air temperature katika intervals maalum:

\[ Z_m = \left[ T_m-\frac{\Delta T}{2}, T_m+\frac{\Delta T}{2} \right) \]

Data katika kila outdoor-temperature interval zilikusanywa katika D_m subset. Ikiwa interval ilikuwa na samples chini ya tatu, iliondolewa kama temporary noise:

\[ n_m \geq n_{\mathrm{th}}, \qquad n_{\mathrm{th}}=3 \]

Variables katika valid intervals zilismooth kwa arithmetic mean:

\[ \overline{y}_m = \frac{1}{n_m} \sum_{k=1}^{n_m} y_{m,k} \]

Approach hii inalenga kuonyesha average na relatively stable system behavior chini ya similar outdoor-air conditions badala ya individual hourly points.

Supply-water temperature inarekebishwaje?

Kwa kila operating region, linear baseline model kati ya secondary supply-water temperature na outdoor-air temperature iliundwa:

\[ T_{2g,r}=a_rT_{\mathrm{out}}+b_r \]

Model coefficients zilikadiriwa kwa least-squares method:

\[ J(a_r,b_r)= \sum_{j=1}^{M} \left[ T_{2g,j}- \left(a_rT_{\mathrm{out},j}+b_r\right) \right]^2 \rightarrow \min \]

Temperature difference kati ya baseline curve ya existing heat-oversupply region na target-region curve inakokotolewa:

\[ \Delta T(T_{\mathrm{out}}) = T_{2g,L}-T_{2g,H} = (a_L-a_H)T_{\mathrm{out}}+(b_L-b_H) \]

New supply temperature hupatikana kwa kutoa correction hii kutoka current value:

\[ T'_{2g,L}=T_{2g,L}-\Delta T(T_{\mathrm{out}}) \]

Correction hapa si constant. Ikiwa heat-oversupply potential inaongezeka kadiri weather inavyopata joto, larger temperature reduction hutumika; katika severe cold, ambapo actual user heat requirement ni high, smaller reduction hutumika.

Ni nini kilipatikana katika B na C temperature regions?

Kielelezo 11 katika ukurasa wa 26 wa PDF kinatoa models zifuatazo kwa Bc na Ba regions:

\[ T_{2g,\mathrm{Bc}} = -0.86T_{\mathrm{out}}+55.2, \qquad R^2=0.83 \]

\[ T_{2g,\mathrm{Ba}} = -0.46T_{\mathrm{out}}+56.5, \qquad R^2=0.72 \]

Outdoor-air temperature inapopanda zaidi ya −3 °C, supply-water temperature katika Ba region hubaki juu kuliko Bc baseline yenye high valve opening ratio. Proposed reduction kwa transition kutoka Ba kwenda Bc ni:

\[ \Delta T_{\mathrm{Ba}}(T_{\mathrm{out}}) = 0.40T_{\mathrm{out}}+1.30, \qquad T_{\mathrm{out}}>-3^\circ\mathrm{C} \]

Kulingana na formula hii, outdoor air ikiwa 0 °C, temperature hupunguzwa takribani 1,3 °C; ikiwa 8 °C, hupunguzwa takribani 4,5 °C.

Baseline models zilizotolewa kwa C column ni:

\[ T_{2g,\mathrm{Cb}} = -0.38T_{\mathrm{out}}+51.5, \qquad R^2=0.72 \]

\[ T_{2g,\mathrm{Ca}} = -0.46T_{\mathrm{out}}+54.0, \qquad R^2=0.82 \]

Kwa transition kutoka Ca kwenda Cb:

\[ \Delta T_{\mathrm{Ca}}(T_{\mathrm{out}}) = -0.08T_{\mathrm{out}}+2.50, \qquad T_{\mathrm{out}}>0^\circ\mathrm{C} \]

imependekezwa. Ingawa reduction amount katika formula hii hupungua kiasi kadiri outdoor temperature inavyoongezeka, heat saving katika C region hubaki significant kwa sababu ya high initial temperature na high closed-valve ratio.

Medium valve-opening na low-temperature regions zinarekebishwaje?

Correction formula ya Bb region relative na target Bc region ni:

\[ \Delta T_{\mathrm{Bb}}(T_{\mathrm{out}}) = 0.60T_{\mathrm{out}}+2.20, \qquad T_{\mathrm{out}}>-3^\circ\mathrm{C} \]

Kielelezo 14 kinaonyesha kwamba Bb baseline iko karibu zaidi na Bc kuliko Ba baseline. Hii imetafsiriwa kwamba kadiri valve opening ratio inavyoongezeka, system yenyewe hukaribia demand-based ideal temperature curve.

Katika low-temperature Ab region, proposed correction ni ndogo zaidi:

\[ \Delta T_{\mathrm{Ab}}(T_{\mathrm{out}}) = -0.17T_{\mathrm{out}}+0.02, \qquad T_{\mathrm{out}}<0^\circ\mathrm{C} \]

Katika severe cold, valves tayari hubaki open kwa muda mrefu, hivyo heat-oversupply potential ni limited. Kwa hiyo utafiti unatumia more conservative control approach inayolinda thermal comfort katika region hii.

Network hydraulic resistance ilibainishwaje?

Approximate hydraulic relation katika heating networks inaelezwa kama:

\[ \Delta P=S G^2 \]

• ΔP: System differential pressure, Pa,
• S: Equivalent hydraulic-resistance coefficient, Pa/(m³/saa)²,
• G: Secondary-network flow rate, m³/saa.

Resistance iliyopimwa wakati valves nyingi zimefungwa inaweza kuwakilisha terminal-valve throttling zaidi kuliko structural resistance ya pipe network. Kwa hiyo watafiti walichagua data kutoka Ac na Bc regions ambapo valve opening ratio ilikuwa zaidi ya %76,5.

Stable time series tisa zilitumika. Katika series hizi:

• Consecutive indoor-temperature differences ziliwekwa ndani ya ±0,1 °C,
• Consecutive flow differences ziliwekwa ndani ya ±2 m³/saa.

Average resistance coefficient ya kila time series ilikokotolewa na largest value ikachaguliwa ili kubaki upande salama:

\[ S_{\mathrm{opt}}=\max\{S_1,S_2,\ldots,S_r\} \]

Value iliyobainishwa ilikuwa:

\[ S_{\mathrm{opt}} = 1.24\ \mathrm{Pa/(m^3/h)^2} \]

. Kielelezo 16 katika ukurasa wa 30 wa PDF kinaonyesha results za groups tisa zikikusanyika karibu 1,22–1,24 Pa/(m³/saa)².

Target flow rates zilibainishwaje?

Kwa kutumia high valve-opening-ratio data za kila temperature column, average flow rates ndani ya 24-hour cycle zilikokotolewa. Ili kupunguza pump energy, lowest value ya averages hizi ilichaguliwa kama target:

\[ G_{t,\mathrm{opt}} = \min\{G_1,G_2,\ldots,G_l\} \]

Kwa kuwa hakuna high-valve-opening samples za kutosha katika Cc region, neighboring Cb region ilitumika. Choice hii inaeleweka kwa implementation, lakini inamaanisha target flow ya C column haikubainishwa moja kwa moja kutoka ideal Cc operating data.

Differential-pressure settings zilikokotolewaje?

Kwa kila region:

\[ \Delta P_{t,\mathrm{target}} = S_{\mathrm{opt}}G_{t,\mathrm{opt}}^2 \]

relation ilitumika.

Katika existing operation, differential pressure iliripotiwa kuwa measured karibu 245–253 kPa. Katika proposed method, value hubadilishwa kati ya 226–244 kPa kulingana na outdoor na indoor temperature region. Katika hottest C column, lower pressure inapendekezwa kwa sababu demand ni low; katika cold A column, higher pressure inapendekezwa ili kulinda user heat demand.

Case area ina sifa gani?

Utafiti unatokana na district heating system ya university staff residential campus mjini Tianjin. Kielelezo 5 katika ukurasa wa 20 wa PDF kinaonyesha distribution ya network kutoka boiler room na substation kwenda buildings.

Kulingana na Kielelezo 6, daily average secondary flow ilifikia peak ya 457,45 m³/saa tarehe 13 Desemba. Daily flow-change range ilikuwa 4,4–31 m³/saa, average daily change karibu 15 m³/saa, na total seasonal variation amplitude 56,8 m³/saa.

Kielelezo 7 kinaonyesha resistance coefficient ikibadilika katika opposite direction na flow. Katika severe-cold period, values zilibaki stable zaidi ndani ya 1,2–1,3 Pa/(m³/saa)²; mwanzoni na mwishoni mwa season zilipanda zaidi ya 1,3 na wakati mwingine zikakaribia 1,7. Behavior hii inaendana na apparent network resistance kuongezeka wakati valves zinafunga mara nyingi zaidi katika warm weather.

Heat saving imefafanuliwaje?

Heat-saving ratio ni difference kati ya heat consumption kabla na baada ya adjustment, ikigawanywa kwa initial consumption:

\[ \delta_Q = \frac{Q_a-Q_b}{Q_a} \times 100\% \]

• Q_a: Heat consumption kabla ya adjustment, GJ,
• Q_b: Heat consumption baada ya adjustment, GJ.

Kielelezo 18 katika ukurasa wa 34 wa PDF kinaonyesha saving ikibadilika kwa kiasi kikubwa kulingana na outdoor-air temperature na region:

• Ba na Bb: Outdoor air ilipopanda kutoka −3 °C hadi takribani 7–9 °C, saving iliongezeka karibu linearly na maximum value ikafikia takribani %24.
• Common 4 °C condition: Takribani %9,9 heat saving ilikokotolewa kwa system.
• Ab: Kati ya −9 na −1 °C, saving ilibaki chini ya %4 na ilipungua kadiri weather ilivyokaribia 0 °C.
• Ca: Kati ya 0–14 °C, saving ilibaki mostly katika %7–9 range; increase kubwa zaidi ilionekana juu ya 14 °C.

Result hii inaonyesha kwamba method hutoa gain kubwa zaidi katika oversupply conditions ambapo high-temperature water inaendelea kutumwa katika warm weather. Katika severe cold, valves tayari ziko mostly open, hivyo safe range ya temperature reduction ni ndogo.

Electricity saving ilikokotolewaje?

Electricity-saving ratio imefafanuliwa kama:

\[ \delta_W = \frac{W'_a-W'_b}{W'_a} \times 100\% \]

• W′_a: System electricity consumption kabla ya adjustment, GJ,
• W′_b: System electricity consumption baada ya adjustment, GJ.

Highest electricity saving ilikokotolewa katika high-temperature C regions ambapo lower target flow na lower differential pressure zinaweza kutumika. Katika cold A regions, pressure iliweza kupunguzwa kwa kiasi kidogo kwa sababu user demand na hydraulic safety vinapaswa kulindwa; electricity saving ikabaki karibu %1,7.

Main results zinazoungwa mkono na utafiti ni zipi?

• Individual na discrete states za on-off valves zinaweza kubadilishwa kuwa central demand indicator zinapotumiwa pamoja na open duration na heating area.
• Joint evaluation ya average indoor temperature na valve opening ratio hutoa more informative supply-demand classification kuliko kuangalia valve state pekee.
• High indoor temperature na low valve opening ratio zinahusishwa na heat oversupply na strong hydraulic fluctuation katika system iliyochunguzwa.
• Kupunguza supply-water temperature kulingana na outdoor weather na operating region hutoa control target inayoweza kuhamisha operating points kwenda regions zenye higher valve opening ratio.
• Stable high-valve-opening periods zinaweza kutumika kubainisha pipe-network hydraulic resistance ambayo imeathiriwa kidogo na terminal-valve throttling.
• Region-specific 226–244 kPa settings badala ya constant differential pressure hupunguza calculated pump electricity consumption.

Utafiti hauonyeshi nini?

• Performance ya method katika climates tofauti, building types tofauti au network sizes tofauti haijavalidate.
• Results hazijajaribiwa kwa independent second campus au external validation dataset.
• Hakuna confidence interval, standard deviation, p value au other statistical uncertainty measure iliyotolewa kwa saving ratios.
• Detailed distribution ya jinsi users' thermal comfort ilivyohifadhiwa baada ya adjustment haijaripotiwa.
• Utafiti hauelezi wazi full closed-loop control ilitumika physically kwa muda gani katika entire heating season na comparison periods zili-match vipi.
• Maximum %24 heat saving na %10,04 electricity saving si average values zinazotarajiwa katika all regions au all days.
• Control safety dhidi ya valve communication failure, sensor drift, data loss au users kufungua windows haijachunguzwa.
• Carbon emissions, investment cost, payback period au maintenance cost hazijakokotolewa.
• Method haioptimize user valves moja kwa moja wala kuingilia user settings.

Nguvu za utafiti ni zipi?

Nguvu muhimu zaidi ya utafiti ni kuunganisha classical weather compensation inayotegemea outdoor-air temperature pekee na real user behavior. Kutumia field data kutoka full heating season kunazuia method kutegemea theoretical pipe-network model pekee.

Kushughulikia thermal na hydraulic control katika framework ileile pia ni muhimu. Kupunguza supply-water temperature hupunguza overheating, huku kurekebisha differential pressure kulingana na target flow kulenga pump energy na pressure fluctuations. Decisions hizi mbili si independent; zinatolewa kutoka user-state map ileile.

Utafiti pia unajaribu kutenganisha pipe resistance na terminal-valve throttling effect kadiri iwezekanavyo kwa kuchagua stable high-valve-opening periods. Kutumia explicit thresholds za temperature na flow stability katika time series tisa huongeza traceability ya hydraulic-resistance selection.

Main limitations ni zipi?

• Utafiti ni preprint ambayo haijapitia peer review.
• Case study imewekewa mipaka ya university campus moja.
• Raw data ni confidential na haiwezi kuthibitishwa na independent researchers.
• Total user count, total heated area na technical accuracy za sensors hazijaripotiwa.
• Handling ya missing data, outliers na communication interruptions haijaelezwa kwa kina.
• Quartile boundaries ni dataset-specific; values za 19,5 °C, 20,4 °C, %66 na %76,5 hazipaswi kuhamishwa directly kwenda systems nyingine.
• Linear temperature baseline models zinategemea limited number ya temperature-interval averages.
• R² values za baadhi ya models ni takribani 0,63–0,83, hivyo kuna unexplained variability.
• Kwa sababu data za Cc region hazitoshi, target flow ya C column ilitolewa kutoka neighboring Cb region.
• Target flow ya A column ni 443 m³/saa katika text na 444 m³/saa katika final table.
• Expression “×100%” katika average-temperature equation inaonekana dimensionally inconsistent.
• Field safety boundaries za control strategy dhidi ya risks kama freezing, insufficient heating au pressure loss kwenye farthest terminal hazijaelezwa kwa kina.

Utafiti una maana gani kwa past, present na future?

Traditional district-heating control kwa kawaida huendeshwa kwa outdoor-air temperature, constant flow au constant pressure. Smart thermostatic valves hutoa flexibility katika user level, lakini bila information link kati ya terminal behavior na central production, flexibility hii inaweza isiakisi kikamilifu katika system efficiency.

Utafiti huu unabadilisha discrete actions nyingi za users kuwa macro indicators mbili na kuzifanya readable kwa central operation. Mchango wake ni kuunganisha user-comfort signal na valve-demand signal kwenye map moja na kuunganisha map position moja kwa moja na supply-temperature na pump-pressure decisions.

Katika future, method inahitaji validation katika cities tofauti, building stocks tofauti na independent heating seasons. Real energy meters, comfort distributions na control stability vinapaswa kupimwa pamoja katika closed-loop field experiments. Kuchapisha anonymized datasets huku data privacy ikilindwa kunaweza kuongeza reproducibility. Pia, adaptive models zinazokokotoa uncertainty zinaweza kutumika badala ya linear baseline models; hata hivyo, development kama hiyo haijajaribiwa katika current study.

Mbinu na Matokeo ya Utafiti

Muhtasari wa kiufundi wa method

Region-specific temperature corrections

Main numerical findings

Scientific message ya figures na tables

• Kielelezo 1: Kinaonyesha terminal data-collection layer na substation temperature/pressure control katika hierarchy moja.
• Kielelezo 2: Kinaeleza time-dependent response ya on-off valve na room temperature wakati user set temperature inabadilika.
• Kielelezo 3: Kinaonyesha average indoor temperature na valve opening ratio zikigawanywa katika operating regions tisa.
• Kielelezo 4: Kinaonyesha intersections kati ya pump curve na varying network-resistance curves katika constant na variable differential-pressure control.
• Kielelezo 5: Kinaonyesha distribution ya buildings 48 katika case-area heating network.
• Kielelezo 6: Kinaonyesha average, maximum na minimum daily flows na daily change range katika heating season.
• Kielelezo 7: Kinaonyesha network-resistance coefficient ikishuka katikati ya season na kuongezeka mwanzoni na mwishoni mwa season.
• Kielelezo 8: Kinaonyesha state map katika real data yenye boundaries za 19,5–20,4 °C na %66–%76,5.
• Kielelezo 9: Kinaonyesha ni wakati gani operating regions tisa zilitokea katika heating season.
• Kielelezo 10: Kinalinganisha distributions za outdoor temperature, supply-water temperature, flow na resistance coefficient katika regions tisa.
• Vielelezo 11–14: Vinaonyesha outdoor-temperature–supply-water-temperature baselines na applicable reduction amounts kwa regions tofauti.
• Kielelezo 15: Kinathibitisha temperature na flow stability conditions za time series tisa zilizochaguliwa kwa hydraulic resistance.
• Kielelezo 16: Kinaonyesha resistance results za 1,22–1,24 Pa/(m³/saa)² kutoka time series tisa.
• Kielelezo 17: Kinaonyesha 24-hour target-flow distributions kwa A, B na C temperature regions.
• Kielelezo 18: Kinaonyesha heat saving inategemea outdoor-air temperature na initial oversupply.
• Kielelezo 19: Kinaonyesha electricity saving ikiongezeka wazi katika high-temperature C regions.
• Jedwali 1: Linafupisha thermal, hydraulic na operating characteristics za regions tisa.
• Jedwali 2: Linatoa applicable temperature-reduction equations na meteorological conditions kwa regions nne.
• Jedwali 3: Linatoa regional target flows na differential-pressure values.

Maelezo ya Chanzo na Mbinu

Kichwa kamili cha asili cha utafiti: A hierarchical terminal-substation coordinated regulation framework for district heating: aggregating decentralized on-off behaviors for centralized thermal-hydraulic reset

Waandishi na mpangilio wao katika PDF: Shanshan Cao; Wenxu Wang; Wenjing Ma; Yongpeng Yan; Chunhua Sun; Xiangdong Wu

Co-first author au equal contribution: PDF haina co-first authorship au equal-contribution statement.

Corresponding author: Chunhua Sun

Barua pepe ya corresponding author: sunchunhua_2025@sina.com

Institutional affiliations:

1. School of Energy and Environmental Engineering, Hebei University of Technology, Tianjin 300401, China
2. MCC Jingcheng Engineering Technology Co. Ltd, Beijing 100176, China
3. North China Municipal Engineering Design and Research Institute, Tianjin 300074, China
4. Hebei Gongda Keya Energy Technology Co. Ltd, Shijiazhuang 050000, China

DOI: 10.2139/ssrn.6946539

Publication platform: SSRN

SSRN upload date: 15 Juni 2026

Publication year: 2026

Official link:Ukurasa wa utafiti wa SSRN

Journal au conference: Peer-reviewed journal au conference publication haijathibitishwa kwa version hii.

Original journal publisher: Peer-reviewed journal publisher haijathibitishwa kwa version hii.

Source type: Preprint research article ya district-heating control na case analysis inayotegemea real operating data

Peer-review status: Utafiti haujapitia peer review.

SSRN–PDF institution-information note: Institution metadata kwenye official SSRN page ni incomplete kwa baadhi ya authors au hailandi kikamilifu na institutions katika PDF. Katika makala hii, institutions na author order kwenye first page ya PDF zimetumika kama msingi.

Author contributions: Shanshan Cao alifanya conceptualization, methodology, supervision, funding, resources, review na editing. Wenxu Wang alichangia methodology, software, first draft na investigation. Wenjing Ma alichangia methodology na first draft. Yongpeng Yan alifanya methodology na supervision. Chunhua Sun alichangia supervision, first draft na resources. Xiangdong Wu alitoa resources na supervision support.

Funding: Utafiti uliungwa mkono na Shijiazhuang Municipal Bureau of Science and Technology chini ya project “Research and Development and Application of Intelligent Control Technology for Multi-energy Complementary Heat Supply System” kwa number 241230113A.

Field na data support: Hebei Gongda Keya Energy Technology Co., Ltd. ilitoa required data na field-study platform.

Conflict of interest: Waandishi walitangaza kwamba hakuna known financial interest au personal relationship inayoweza kuathiri utafiti.

Data access: Data zilizotumika katika utafiti ni confidential na hazipatikani publicly.

Makala hii ya Kituruki imeandaliwa baada ya kuchunguza uploaded PDF kuanzia mwanzo hadi mwisho, ikijumuisha text, equations, tables, state maps, regression graphs, hydraulic schematics na energy-saving figures. Hakuna claim mpya ya energy saving, comfort, economic return au field success iliyoongezwa nje ya PDF.

Katika text, target flow ya A column imeelezwa kuwa 443 m³/saa, wakati final Table 3 inatumia 444 m³/saa. Kwa kuwa final pressure calculation ya 244 kPa inaendana na 444 m³/saa, value ya final Table 3 imetumika katika technical summary table. Expression “×100%” katika average indoor-temperature equation inaonekana dimensionally inconsistent; arithmetic mean inayoendana na subsequent analyses za utafiti imeelezwa.

Main limitations ni matumizi ya site moja, raw data kuwa confidential, kutoripotiwa kwa user count na sensor accuracy, kutotolewa kwa kina kwa control-implementation timeline, kutokuwepo kwa independent external validation, sample count ndogo katika baadhi ya regions na kutotolewa kwa statistical uncertainty kwa saving results.

Reported %24 heat saving na %10,04 electricity saving ni highest results za system iliyochunguzwa. Hazipaswi kutafsiriwa kama fixed values zitakazopatikana sawa katika every outdoor-air temperature, every building au other district-heating system.

Utafiti huu ni preprint ambayo haijapitia peer review; results zinapaswa kusomwa kwa kuzingatia limitation hii.