Predicting heat demand for a district heating systems

Transcription

1 Internatonal Journal of Energy and Power Engneerng 204; 3(5): Publshed onlne October 20, 204 ( do: 0.648/j.jepe ISSN: X (Prnt); ISSN: X (Onlne) Predctng heat demand for a dstrct heatng systems Krzysztof Wojdyga Faculty of Envronmental Engneerng, Warsaw Unversty of Technology, Warsaw, Poland Emal address: krzysztof.wojdyga@s.pw.edu.pl To cte ths artcle: Krzysztof Wojdyga. Predctng Heat Demand for a Dstrct Heatng Systems. Internatonal Journal of Energy and Power Engneerng. Vol. 3, No. 5, 204, pp do: 0.648/j.jepe Abstract: Poland s one of the heavest users of dstrct heatng systems n Europe, and those dstrct heatng systems are heated manly by coal. Sustanable development of dstrct heatng systems n Poland ncludng mprovng qualty of envronment, economc of heat producton and securty of heat supply s n close connecton wth ncreasng of energy effcency. Heat producton and heat dstrbuton plays mportant role n natonal energy balance. Addtonal ncreasng of energy effcency n dstrct heatng systems need detal forecasts for future heat consumpton n scale of ndvdual dstrct heatng system and for systems n whole country. Accurate forecast gve possblty for ncreasng effcency of heat producton, decreasng fuel consumpton and connected wth t emsson decreasng from combuston products to the atmosphere. Heat producton effcency can be optmzed through the use of approprate procedures for runnng heat sources alongsde short-term heat demand forecastng combned wth preparaton for adjustng heat source work parameters to the predcted heat load for a few hours hence. The artfcal neural networks model delvers good forecastng results. The accuracy of the results depends on the knd of network, ts archtecture, the sze and type of nput data as well as the forecastng perod. Forecastng accuracy wthn a 3-5% margn of s suffcent to steer heat source operatons. Descrbed forecastng methods can be use as a good tool to regulate dstrct heatng networks and heat sources. Keywords: Dstrct Heatng Systems, Heat Demand Predcton. Introducton In urban areas wth hgh densty of demand for heat, the most ratonal and economcal means of heat supply for the nhabtants are dstrct heatng systems. Large sources producng heat for heatng systems are generally equpped wth hgh-effcency unts, lmtng emssons of combuston products nto the atmosphere. The emsson of pollutants from small, local and dspersed sources consumng nferor fuels s hgher n relatve terms than from centralzed sources. Dstrct heatng, a very mportant energy sub-sector for the Polsh economy, supples heat to centralzed heatng systems whch, on average, satsfy 72% of the demand for heat n Polsh ctes[]. In the 990s, Poland embarked on the process of modernzng ts heatng systems. At that tme there was also a reducton n heat demand of over 30% due to the thermomodernzaton of buldngs. The reducton n heat demand was vsble despte numerous new recpents jonng the system. Sustanable development of heatng systems n Poland s closely connected wth further ncreases n energy effcency both on the part of recpents and heat producers. The modernzaton of heat sources, especally n small and medum heatng systems, s lnked wth changes of fuel. Hard coal wll be replaced wth natural gas, bofuel and heat obtaned from other sources. Such dversfcaton of heat sources s especally vsble n Scandnava and wll contnue to develop [2,3]. An mportant element of modernzaton wll be the constructon of small cogeneraton systems equpped wth heat acumulators, whch enable the sources to operate more effcently. The constructon of small cogeneraton sources has already started n Poland. Internatonal oblgatons oblge Poland to take acton to reduce polluton emssons to the atmosphere [4,5,6,7,8]. In the document Polsh Energy Polcy untl 2030 [9] t s assumed that cogenerated electrcty wll double from 25 TWh (6% of producton n 2009) to 50 TWh n Ths wll not be possble wthout buldng new CHP nstallatons. An mportant element that wll allow heat generaton schemes for heatng systems to run more effcently s heat demand forecastng, both at the desgn and operatonal stages of the nstallaton. The monograph [0] presents the results of forecastng water temperature supples for three dstrct heatng substatons. Even before that [] there was a

2 238 Krzysztof Wojdyga: Predctng Heat Demand for a Dstrct Heatng Systems comparson of dynamc forecastng models showng temperature changes n heatng networks. In heatng systems powered by CHP plants equpped wth heat acumulators t s mportant to plan heat generaton so that the maxmum amount of electrcty can be obtaned at the tme when ts prce s the hghest. These ssues were descrbed n artcles [2,3,4]. At present n Poland the sale prce of electrcty s at a stable level and t s practcally ndependent of supply and demand, except for the small volume of electrcty sold on the Warsaw Stock Exchange Energy Market [5]. In comng years we wll note that the prce of electrcty wll become varable on the market and there wll be an mpulse to buld heat acumulators n heatng systems. The paper [6] descrbes an artfcal network predcton method for long term energy consumpton n Greece. The weather forecast naturally plays a key element n heat demand. Heat load predcton wll nevtably depend heavly n future on short term forecastng, partcularly the assessment of chances of extreme weather condtons and ther nfluence on supplyng users wth suffcent quanttes of heat. One way to boost heat producton effcency s to mplement approprate procedures for operatng heat sources alongsde short-term forecastng of heat demand. Emprcal research, coverng many years of data underscorng the weather dependency of thermal power demand, provdes a sold bass for forecastng future heat load demand for dstrct heatng systems under medum and extreme weather condtons. Short-term several-hour forecasts enable provders to adjust heat source parameters at an earler stage so they can antcpate near-term user demand. To ths end the model of artfcal neural networks has been used. Research has been carred out wth the use of varous neural network models, relyng on a set of actual data coverng heat consumpton by a complex of buldngs matched aganst weather condtons over a 0 year perod. 2. Short-Term Forecastng of Thermal Power In a md-term forecast one estmates heat demand and thermal power takng nto consderaton the volume of heated cubc capacty and user demands. Note s also taken of the weather condtons prevalng n the gven heatng season. Wth the above n mnd, one plans the scope of current mantenance, the purchase of fuel and other materals and applances requred for approprate operaton of the system. Fluctuatons n heat and thermal power demand n the heatng system are determned by changes n the weather. Forecasts for thermal power broken down nto day/hour/lessthan-hour slots enable greater ratonalty to be appled to operatng the heat source and to determnng the power and acton tme of the partcular producton unts as well as the on- and off-tmes. Forecasts of a short tme horzon make t possble to react promptly, adjustng the source to unforeseen random events. In cogeneraton plants, where electrcty generaton s closely connected wth heat producton, 48 hours notce must be gven to contract the volume of energy sales at the energy exchange. Consequently, shortfalls or surpluses of contracted electrcty may happen unless an effcent system of demand forecastng for thermal power s put n place. Settng asde possble energy supply dsruptons, frequent msmatches of demand and contracted supply wll cause a consderable drop n revenues from electrcty sales. To delver a meanngful upgrade n heatng systems changes must be made to the processes of regulatng heat sources so as to guarantee the smooth runnng of heatng networks and heat substatons, whch are fully or partally equpped wth automatc follow-up control systems. Quanttatve-qualtatve regulaton has been ntroduced n many heat sources feedng heatng systems, as t facltates optmal control of the heatng system wth a concomtant ncrease n heat supply effcency. One condton for achevng optmzed regulaton of the system s ensurng that the thermal power of the sources s adjusted to the recpents current demand for heat. Ths requres accurate near-term forecastng of heat demand. Forecast accuracy s affected by numerous factors: change n weather condtons (general ar temperature, wnd speed and drecton, sun exposure, precptaton, etc.). perodc changes n condtons of heat offtakers due to changes: day-nght, season, day of the week, etc. random changes connected wth heat offtakers (holdays, techncal gltches). behavoral changes n heat recpents. heat slands and other such heat accumulaton phenomena. The thermal nerta of the system as a whole s large and reacton to dynamc changes s slow. It s nfluenced by the thermal nerta of the ndvdual elements of the heatng system, such as heat sources, transmsson network, dstrbuton network, thermal centers, nternal nstallatons and the walls of buldngs. The lterature lsts many dfferent mathematcal models that descrbe the structures of dynamc systems. These can be dvded nto two groups: analytc and expermental. Models based on theoretcal methods requre the analytc soluton of a system of equatons. In complex systems wth many varables the rght smplfyng assumptons have to be determned n full knowledge that every decson nfluences the accuracy of the results obtaned. Analytc methods are of lttle use when faced wth buldngs wth varous heatng characterstcs cooperatng wth an elaborate heatng system and heat source. Here, t s more approprate to use expermental methods where the parameters of the model have been assgned on the bass of the expermental dentfcaton of the buldng. These can be ether classc graphc methods or new ones based on calculus of probablty. These methods can be used for forecastng n the control and steerng processes as well as n optmzaton and fault fndng systems. Many methods can be used n the forecastng of tme seres. Satsfactory forecastng results have been obtaned through the use of: regressve models (lnear, recurrent)

3 Internatonal Journal of Energy and Power Engneerng 204; 3(5): statstcal models based on tme seres (the ARMAX, ARIMA models) lkelhood RML methods (Recursve Maxmum Lkelhood). 3. Characterstcs of the Buldngs Researched The man campus of the Warsaw Unversty of Technology (WUT) s comprsed of a sectoned-off complex of buldngs. Heat for all the buldngs s suppled by the muncpal heatng system va a central substaton. From there t s drected to the local network feedng the ndvdual buldngs housng classrooms and apartments. The bass for predctng heat consumpton n the future n random weather condtons wll be provded by a large set of actual data recordng heat supply condtons and nformaton about heat consumpton at varous temperatures over many heatng seasons. Annual demand on WUT s man campus for power ordered n 200 was 0 MW (ncludng 0.57 MW for hot water) and the average mult-year actual value of 349 degree days for Warsaw s 76.4 TJ annually. Ths value ncludes demand for hot water whch, on average, s 7.3 TJ annually and consttutes almost 0% of the entre heat demand. Takng nto consderaton the actual mnmum of 2959 and maxmum of 3987 degree days over the last 40 years, the estmated heat demand for WUT s man campus should be between 65.8 and 86.2 TJ. 4. Forecastng wth the Use of Neural Networks Artfcal neural networks (ANN) may provde a means of forecastng values n tme seres. Thanks to propertes such as ease of nput data selecton and good convergence when seekng solutons, ANNs have been used n steerng and control processes. Ths method can be appled n object dentfcaton as well as n forecastng. Frequently, a long tranng perod and the relaton between obtanng good results and the parameters of the tranng method may pose problems for networks utlzng the back-propagaton algorthm. The frst neural network models used n the power sector concerned forecastng values n tme seres (power demand) n power systems. Proposng a relable and confrmed forecast brngs sgnfcant savngs, resultng n betterplanned electrcty producton. Research nto electrcty forecastng focuses ether on ndvdual buldngs [7] or entre power systems. [8] shows the problem of electrcty producton n cogeneraton systems ncludng short-term forecasts for electrcty producton. The paper presents a method for modellng and predctng the effcency of bolers based on measured operatng performance, usng the neural network method [9]. The paper [20] nvestgates the use of ANN modellng to predct fuel consumpton and exhaust emssons of a spark gnton engne. Author [2] presents forecastng for wnd power potental n electrcty producton. Publcatons [22,23,24] compare forecastng usng neural networks wth conventonal statstcal methods of lnear modellng, forecastng usng autoregressve methods and other methods. Model research of neural networks used n forecastng electrcty demand has shown hgh compatblty between realty and the models examned. It s more dffcult to predct demand for heat n dstrct heatng systems, as demand depends largely on the weather, whch s very changeable n many parts of the world. In dstrct heatng, n partcular as regards heat sources, tmely and accurate forecastng of heat power demand may delver sgnfcant commercal advantages. An essental aspect of neural networks s ther applcaton n respect of questons of predctng energy consumpton for the purposes of runnng buldngs. The frst papers on the topcs of heatng and dstrct heatng, ventlaton and ar-condtonng started appearng n scentfc journals n the early 990s. More than 00 dfferent types of artfcal neural network have been tested [25-29] as part of dedcated systems for HVAC dagnostcs n commercal buldngs. A smlar artcle s devoted to the applcaton of ANN to condton montorng and dagnoss of CHP [30]. A large number of papers deal wth steerng and forecastng heat supples for buldngs [3-34]. Papers only rarely concern themselves wth ssues nvolved wth steerng and forecastng heat supples and producton n dstrct heatng systems. There are many publcatons wth analyses presentng predctons of heat and electrcty producton. Polsh papers on dstrct heatng devote lttle tme and space to research nto artfcal neural networks used n steerng processes [35], even less so when t comes to forecastng demand for power and heat. Neural networks owe much of the nterest surroundng them to ntrgung propertes such as: forecastng possbltes, the capablty of classfcaton, adaptaton and self-organzaton of possble nterference reducton. One of the more mportant features of neural networks s ther ablty to learn. In the tranng process, the most mportant factor s a suffcent quantty of nput and output data to descrbe a gven process. Durng the tranng process the neural network acqures features characterstc of ths or that system or process, so after a suffcently long learnng cycle the network becomes a model of the phenomenon under analyss. A neural network taught n ths way s capable of predctng an output sgnal or a sequence of output sgnals for the nput data, whch s extraneous to the data set for tranng. There s no need here for a mathematcal descrpton of the relatons between nput and output sgnals. Tranng condtons depend on many factors, n partcular the manner of presentng nput-output data and the network archtecture. The model of a neuron has been planned on the bass of, and makng use of neuropsychologcal features of a bran cell. The branchng networks of nerve fbres (dendrtes) are connected to the body of a cell contanng a nucleus. Sgnals

4 240 Krzysztof Wojdyga: Predctng Heat Demand for a Dstrct Heatng Systems are passed through electrochemcal processes. Input sgnals are delvered to the cell by means of synapses. Recever ends may be found on dendrtes as well as on the body of other cells. An output sgnal from a neuron s carred by means of an axon and ts numerous branches. The number of neurons n the nput and output layers depends on the external condtons of the problem under consderaton. The number of neurons n the layers and the number of hdden layers s generally estmated expermentally n such a way as to mnmze the generalzaton. of many subgroups, each focusng around the determned pattern. Statstcal varablty must be represented approprately wthn each class. The optmzaton of the objectve functon method s used n the network learnng procedure. Supervsed learnng wth a teacher s the most effectve method of teachng one-drectonal sgmod networks [37]. For the tranng par (x, d), the defnton of the objectve functon takes the form of the mean square : M 2 E = ( y d) (2) 2 = where y=f(u), f s a sgmod functon. For many tranng pars (x(j), d(j)) for j =,2,..., p the defnton of the objectve functon assumes the form: Fgure. Model of a neuron. u = N j = w j x j + B In the above model of a neuron (Fgure ) there s a summaton element wth nput sgnals x, x 2,..., x N, consttutng an nput vector x = [ x x x ] T 2 N. The components of vector x are multpled by the weghts matched W, W2,..., WN. B s the bas assocated wth the node. Out of the summaton comes sgnal u. Durng tests, as an actvaton functon, a sgmod functon s accepted, consttutng the approxmaton of a step functon. Gradent algorthms are used n the tranng process, as they are consdered the most effectve for learnng purposes [36]. In preparng data sets to tran neural networks, we consdered the type of sgnals used at the nput and output of the networks, consttutng an nput and output vector and determned by the sze of the data set for tranng. In the research under consderaton, for the majorty of the neural networks the nput sgnal was formed by the external ar temperature, whle the output sgnal was ts correspondng heat power. In the case of classfcaton: at the nput of the network the elements of the object were descrbed, whereas at the output, the result of the classfcaton was stated. In the case of the dentfcaton of dynamc objects, at the nput of the network the value of an nput sgnal (of the object) was gven at the tme tn - u(tn ) and at drectly precedng tmes u(tn-), u(tn-2),..., whle at the output, the value of an output sgnal (of the object) at a gven tme was y(tn). In the case of predctng values of tme seres at the nput of the network, sgnals at the tme tn-s(tn) and at precedng tmes s(tn-), s(tn-2),..., s(tn-m), s(tn-m-), s(tn-m-2)..., where m stands for a certan perod of tme (e.g. 24 hours) at the output foreseen sgnal values s(tn+), s(tn+2),... etc. Durng research one can contnuously make adjustments to nput and output sgnals. When choosng the sze of the set, we consdered on the one hand the network s tranng speed and, on the other, the precson of tranng. In general, data for tranng wll consst () p M 2 E = ( y( j) d( j)) (3) 2 = = Durng ths research the method used was based on the choce of drecton conformng to the drecton of a negatve gradent: the algorthm of the steepest descent. In the second part of the research, the RBT-type of networks was used (Radal Base Functons). In these types of networks a latent neuron plays the functon, radally changng around the selected center c. The role of the latent neuron s the radal mrrorng of the space around one pont or many ponts. Radal-type networks are complementary to sgmod networks [37]. A sgmod neuron consttutes a knd of hyper-plane dvdng the mult-dmensonal space nto two parts for whch the followng condton s satsfed: x j j j j j W >0 or W x < 0 j (4) A radal neuron consttutes a knd of hyper-sphere n the mddle of whch there s the central pont. The use of radal neurons n modellng means that for the case of radal symmetry of the data set, the quantty of data requred for classfcaton purposes s reduced consderably. In the case of an excessve quantty of data for tranng, the system becomes re-dmensoned wth too great a number of degrees of freedom, whch n effect leads to lower generalzng abltes for the network. As a result, t s necessary to ntroduce addtonal tes to lmt the degrees of freedom for gven parameters. Methods of regularzaton are used for ths purpose. Green s functons are used most frequently, of whch Gauss s functon s the best. The algorthms defned serve as the bass for selectng the quantty of base functons (e.g. Gram-Schmdt ortogonalzaton), though n ths research an emprcal selecton was made of the quantty of neurons n the hdden layer. Ths research was conducted on the actual buldng complex on WUT s man campus. Its dynamc thermal propertes were determned wth a vew to ad the forecastng of heat power demand requred for heatng the buldngs at a

5 Internatonal Journal of Energy and Power Engneerng 204; 3(5): chosen level, ths beng manly related to weather condtons. The analyss of the phenomena occurrng n one buldng under consderaton and the synthess of ths buldng s model wll be carred out on the bass of a tme seres of physcal quanttes connected wth the process of heatng the buldngs. In effect, the model bult (or rather, seeng as neural networks are used as a tool, taught) serves to forecast heat power demand n the form of a tme seres (power values at gven tmes). The preparaton of nput data for the smulaton research of the model of dentfcaton of the buldng complex and predcton of heat consumpton by these buldngs was made possble by the pre-exstng computerzed system for automatcally regulatng and montorng WUT s man campus. The system was launched n November 995 and covers the heatng system as well as 6 heat substatons on WUT s man campus and a few substatons off campus. Each heat substaton n the buldng s equpped wth a system to automatcally regulate the temperature, feedng the central heatng nstallaton dependng on external temperature and the system that measures heat consumpton. Identfcaton of the buldng complex was performed usng data obtaned from the man heat gauge. Data such as temperature, pressure, flows and heat power were read at a frequency of -0 mnutes and then averaged and commtted to memory every hour. These hourly quanttes were used to teach and test an artfcal neural network. The data gatherng took place from 995 to In order to teach the network the dynamcs of a system such as the buldng complex, n whch external ar temperature consttutes an nput sgnal and heat power demand s an output sgnal, the followng approach was used. It was assumed: at the output of the network one sgnal heat power taken md,h (power was taken on day d at hour h), at the nput of the network the values of power taken on the same day one hour, two and three hours earler (md,h-, md,h-2, md,h-3), power taken on the prevous day, respectvely, md-,h, md-,h-, md-,h-2, md-,h-3, power taken two days earler at approprate tmes md-2,h, md-2,h-, md-2,h-2, md-2,h-3 and the temperature on the followng days and tmes: td,h, td,h-, td,h-2, td,h-3, td-,h, td-,h-, td-,h-2, td-,h-3, td-2,h, td-2,h-, td-2,h-2, td-2,h-3. In the research t was assumed, on the bass of general premses, that a neural network wth one hdden layer was n operaton. A seres of experments was carred out, teachng and testng networks wth a dfferng number of neurons n the hdden layer. It was establshed that the best effects were acheved wth 25 neurons n the hdden layer of the network. The confguraton of the network for research was as follows: 23 neurons n the nput layer, 25 neurons n the hdden layer and one neuron n the output layer. Only networks wth back-propagaton of and one hdden layer were used n the research. The number of nputs, neurons n the hdden layer and the number of the network s outputs, for each of the varants consdered, are the outcome of the manner of presentng the data at the nput and output of the network. The tests were performed for 2 dfferent cases: 4.. Case Four dfferent neural networks were taught and tested. Followng the results of research conducted earler that tested heat power demand predcton for a buldng complex, new research was carred out usng for tranng purposes an extended database coverng 5 heatng seasons. For ths purpose, a model of a neural network wth back-propagaton of wth one hdden layer was used. The number of nputs, the number of neurons n the hdden layer and the number of outputs of the network for each of the varants consdered result from the means of the presentaton of the data at the nput and output of the network. The nput vector covered 4 types of data and n the hdden layer there were 8 neurons. In the research under consderaton, appearng at the nput are the followng data: external ar temperature and heat power taken at tmes (d stands for day, h stands for hour): d,h, d,h-, d,h-2, d-,h+, d-,h, d-,h-, d-,h- 2, d-2,h+, d-2,h, d-2,h-, d-2,h-2. At the output of the partcular networks, the value of heat power was expected at one hour (d,h+), two hours (d,h+2) and three hours (d,h+3) n advance. The results of the tests run on the networks taught are shown n Fgures 2-4. Fgure 2. Predcted heat power demand hour ahead of tme. Fgure 3. Predcted heat power demand 2 hours ahead of tme.

6 242 Krzysztof Wojdyga: Predctng Heat Demand for a Dstrct Heatng Systems 4.2. Case 2 Fgure 4. Predcted heat power demand 3 hours ahead of tme. The coeffcent of varaton (CV) s often used as an accuracy measure. The CV s a common metrc used for neural networks [24]. Another metrc can be correlaton coeffcent R2 or cross lnear Pearson correlaton coeffcent between observed and predcted date. CV and R2 were estmated for case : CV (4.6 %, 6.6 % and 7.3 %) and R2 (0.88, 0.84, 0.82). The results are good, ndcatng that the neural network parameters were well-selected. The accuracy and qualty of the forecast can be estmated usng MSE (mean square ), RMS (root mean ), In case 2, for the same nput data, neural network models were compared. The comparson concerned the heat consumpton model two days earler and one day earler wth the model analyzng heat consumpton only the day before. The am was to dscover whether nformaton about heat consumpton n the more dstant past sgnfcantly nfluences the heat consumpton forecast. Usng nput data, the research tested many dfferent heat demand forecast varants for WUT s man campus buldngs. The calculated heat consumpton values were compared to actual values. Fgure 5 shows the result of a one-hour-advance forecast of heat consumpton. The data for tranng and testng ncluded changes n temperature and heat power on the prevous day. Table. Forecast s, 2 and 3 hours ahead of tme. MAPE Mean Max. Net--hour-advance forecastng 2.9% 0.6% 2.% Net-2-hour-advance forecastng 4.%.5% 2.9% Net-3-hour-advance forecastng 4.7% 4.% 6.5% MRE (mean relatve ) and others. It s common practce to resort to the Mean Absolute Percentage Error (MAPE) summary measure. It s defned as follows: MAPE = n n = y d y 00% where d s the predcted value, y s the actual value of heat power, n determnes the scope for whch one estmates forecast. The followng were calculated for all research tests: the mean dfference between the actual and predcted value, takng nto consderaton the devaton mark for the entre measured perod and the maxmum forecast estmated for one hour.the MAPE s, dependng on the forecast s tme advance, are n the range 2.9 to 4.7%. Table shows the calculated s for forecasts of heat power demand, 2, and 3 hours n advance. The longer the forecast perod, the lower the accuracy of the forecast. For one-hour- and two-hour-advance forecasts, the mean relatve of 0.6% and.5% proves that the neural network s well adjusted to the characterstcs of the object under nvestgaton. That s ndcatve of the fact that heat power demand averaged for the entre measured perod s mplemented wth hgh accuracy. (5) Fgure 5. Heat power predcted hour ahead of tme for a neural network (d-). The results presented n Fgure 5 can be compared wth the results from Fgure 6, whch were obtaned for the same temperature and heat power values though ncludng changes gong back one and two days. Fgure 6. Heat power predcted hour ahead of tme neural network (d-, d-2). Coeffcent CV and coeffcent R2 were also calculated for case 2. The results were good: CV (2.6 %, 5.3 %) and R2 (0.97, 0.90). Errors have been calculated for all cases of heat consumpton forecasts and these are shown n Table 2.

7 Internatonal Journal of Energy and Power Engneerng 204; 3(5): Table 2. Forecast s for a network ncludng changes day earler and and 2 days earler. MAPE Mean Max. Net ncludng changes on d and d- 2.% 0.5% 5.6% Net ncludng changes on d, d- and d-2 4.2%.5% 7% The for the forecast based on the reference gong back one day was 2.%, and for the forecast gong back one and two days t was twce as bg, at 4.2%. Heat demand forecastng s also possble n extreme weather condtons. That entals modellng temperature changes so as to determne heat consumpton n these condtons. Ths may consttute the bass for an update of the power order placed wth the dstrct heatng company. The condton necessary for usng ths method s a very large data set for learnng, a long tranng perod and a good correlaton between obtanng results and the parameters of the tranng method. Good results prove that the tests the neural network went through were successful. The neural network very accurately reflects the heatng characterstcs of the buldngs on WUT s man campus. 5. Summary Ths research has shown that the forecastng methods employed are a useful tool for steerng heat networks and heat sources. The possblty of estmatng heat demand a few hours n advance enables optmal determnaton of the quantty of heat energy to be produced. Adjustng heat producton to current demand may boost producton system effcency by as much as a few percent. The use of a model based on artfcal neural networks also produced good forecastng results. The choce of the two knds of networks, namely the back-propagaton type and the RBF type (Radal Base Functons), proved approprate n lght of the results obtaned from the experments. The accuracy of the results obtaned falls n the range of 3-5%, dependng on the knd of network, ts archtecture, the sze and type of nput data and the forecast perod. For the purposes of steerng the heat source, ths level of accuracy s suffcent. To satsfy the necessary condton for forecastng heat consumpton wth the use of an artfcal neural network one needs to possess a database of the man heatng parameters of a dstrct heatng system coverng a few years of operaton. Research nto forecastng the load of a dstrct heatng system may be performed for as long as new tools mprovng predcton qualty appear. Neural networks undoubtedly open up new vstas for the dscplne and mert use n real lfe stuatons. Symbols B bases, d forecast value, E mean square [%], M number of tranng patterns, md,h heat power taken on day d, at hour h, p number of output neurons, td,h external temperature on day d, at hour h, t computatonal nternal temperature [oc], u output sgnal, Wj weght for nput sgnal, x emprcal value of random varable, y emprcal value of random varable, References [] Report of the Presdent of the Energy Regulatory Offce Heat n Numbers w_lczbach.html (n Polsh). [2] Ostergaard P.A., Lund H., A renewable energy system n Frederkshavn usng low-temperature geothermal energy for dstrct heatng. Appled Energy 88 (20) [3] Danestg M., Gebremehdn A., Karlsson B., Stockholm CHP potental-an opportunty for CO 2 reductons? Energy Polcy 35 (2007) [4] Drectve 200/75EU of the European Parlament and of the Councl of 24 November 200 on ndustral emssons (ntegrated polluton and control) (Recast). [5] Drectve 2009/29EC of the European Parlament and of the Councl of 23 Aprl 2009 amendng Drectve 2003/87/EC so as to mprove and extend the greenhouse gas emsson allowance tradng scheme of the Communty. [6] Drectve 2002/9/EC of the European Parlament and of the Councl of 6 December 2002 on the energy performance of buldngs. [7] Drectve 2004/8/EC of the European Parlament and of the Councl of February 2004 on the promoton of cogeneraton based on a useful heat demand n the nternal energy market and amendng Drectve 92/42/EEC. [8] Drectve 2006/32EC of the European Parlament and of the Councl of 5 Aprl 2006 on energy end-use effcency and energy servces and repealng Councl Drectve 93/76/EEC. [9] Energy Polcy for Poland 2030 from 0 of November 2009 (Mnstry of Economy n Polsh). [0] Stevanovc V.D. et al, Predcton of thermal transents n dstrct heatng systems, Energy Converson and Management 50 (2009) [] Larsen H.V. et al, A comparson of aggregated models for smulaton and operatonal optmzaton of dstrct heatng networks, Energy Converson and Management 45 (2004) [2] Lund H., Andersen A.N., Optmal desgns of small CHP plants n a market wth fluctuatng electrcty prces, Energy Converson and Management 46 (2005) [3] Andersen A.N., Lund H., New CHP partnershps offerng balancng of fluctuatng renewable electrcty productons, Journal of Cleaner Producton 5 (2007)

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