Initial commit

.gitignore

+*.slxc
+slprj/
+*.mexw64

two_rooms_model_init.m

+%% The main initialization script
+
+% Room parameters initialization
+% All the parameters related to a room are collected in the specific
+% structures room201 and room202 with the fields C, R_external, R_internal, and R_interroom.
+
+[room201, room202] = getRoomParameters();
+
+% The external temperature
+[T_external] = getExternalTemperatureRecords();
+
+% We model the rest of the building as a constant temperature.
+T_internal = 16;
+
+% Heating subsystem parameters
+% Given 2x2kW power source, the maximum heating in 4kW
+P_heating_max_W = 4000;
+
+% Ocuppants
+% Each ocuppant produces 80 W
+heat_by_person = 80;
+
+% load the file if no Coder is installed. Otherwise, use the embedded
+% function.
+load('occuppancy_data_room201.mat');
+% max prsons in the room
+persons_max = 30;
+% distribution data has two collumns: time stamps and the probability of a
+% person to be present.
+distribution_data = createPresenceProbabilty();
+
+
+function [room201,room202] = getRoomParameters()
+%GETROOMPARAMETERS Computes RC model parameters for two rooms
+
+% common parameters
+parallelConnection = @(varargin)1/sum(1./[varargin{:}]); %Parallel function for resistors
+
+R_EW1 = 6.6/3600;   % m^2*K*h/J
+R_EW2 = 8.25/3600;  % m^2*K*h/J
+R_IW1 = 0.72/3600;  % m^2*K*h/J 
+R_IW2 = 1.88/3600;  % m^2*K*h/J
+R_F1 = 1.15/3600;   % m^2*K*h/J
+R_R1 = 7.25/3600;   % m^2*K*h/J
+R_W1 = 0.26/3600;   % m^2*K*h/J
+R_W2 = 0.28/3600;   % m^2*K*h/J
+R_W3 = 0.27/3600;   % m^2*K*h/J
+R_W4 = 0.35/3600;   % m^2*K*h/J
+R_D1 = 0.56/3600;   % m^2*K*h/J
+R_D2 = 0.52/3600;   % m^2*K*h/J
+
+%% Room 201
+
+C_1 = 308360; % J/K
+
+R_NE = parallelConnection(R_EW1/21.82,R_EW2/2.07,R_W3/4.28);
+R_NW = parallelConnection(R_EW1/15.58,R_EW2/10.3,R_W1/6.7,R_W2/13.39);
+R_SE = parallelConnection(R_IW2/41.93,R_D2/1.84);
+R_SW = parallelConnection(R_IW1/23.16,R_W4/3.88,R_D1/2.86);
+
+R_F = R_F1/90.64;
+R_R = R_R1/90.64;
+
+R_eq_out = parallelConnection(R_NE,R_NW, R_R);
+R_eq_room_prime = parallelConnection(R_SE,R_F);
+R_eq_corridor = R_SW;
+
+room201 = struct(...
+            'C', C_1,...
+            'R_external', R_eq_out,...
+            'R_internal', R_eq_corridor,...
+            'R_interroom', R_eq_room_prime);
+
+%% Second Room
+
+C_2 = 308360;
+R_NE_2 = parallelConnection(R_EW1/21.82,R_EW2/2.07,R_W3/4.28);
+R_NW_2 = parallelConnection(R_D2/1.84,R_IW2/41.93);
+R_SE_2 = parallelConnection(R_EW1/15.58,R_EW2/10.3,R_W1/6.7,R_W2/13.4);
+R_SW_2 = parallelConnection(R_EW1/9.83,R_EW2/3.24,R_W1/3.35,R_W2/3.35);
+R_F_2 = R_F1/90.64;
+R_R_2 = R_R1/90.64;
+R_eq_out_2 = parallelConnection(R_NE_2,R_SE_2,R_R_2,R_SW_2);
+R_eq_room_prime_2 = parallelConnection(R_NW_2,R_F_2);
+R_eq_corridor_2 = parallelConnection(R_D1/2.86,R_IW1/5.98);
+
+room202 = struct(...
+            'C', C_2,...
+            'R_external', R_eq_out_2,...
+            'R_internal', R_eq_corridor_2,...
+            'R_interroom', R_eq_room_prime_2);
+
+
+end
+
+function [T_external_data] = getExternalTemperatureRecords()
+    T_external_monday = [4 2 1 1 1 0 0 2 5 7 8 10 11 12 12 12 12 11 9 7 6 5 4 4];
+    T_external_tuesday = [3 3 3 3 2 1 1 3 5 8 9 10 11 12 13 13 13 12 9 7 6 5 4 4];
+    T_external_wednesday = [4 2 1 1 1 0 0 1 4 6 8 10 10 11 11 12 11 11 9 7 6 6 6 5];
+    T_external_thursday = [4 4 3 3 1 1 1 2 5 7 9 10 11 12 12 12 12 12 9 7 6 6 5 5];
+    T_external_friday = [5 4 4 3 2 1 1 2 5 6 7 9 11 12 13 12 12 11 10 9 7 6 6 5];
+    T_external_saturday = [5 5 3 3 1 1 2 2 5 6 9 10 10 11 11 12 12 12 10 8 6 6 5 5];
+    T_external_sunday = [4 4 3 2 1 0 0 1 3 5 6 8 10 12 12 12 11 10 9 7 6 5 4 4];
+
+    T_external_week= [T_external_monday T_external_tuesday T_external_wednesday T_external_thursday T_external_friday T_external_saturday T_external_sunday]';
+
+    T_external_time = (1:length(T_external_week))';
+
+    T_external_data = [T_external_time, T_external_week];
+end
+
+function distribution_data = createPresenceProbabilty()
+
+    % for 7*24*60 minutes
+    distribution_data = zeros(7*24*60,2);
+    distribution_data(:,1) = (1:7*24*60)'/60; %in hours
+    
+    % one-day distribution
+    P = zeros(1440, 1);
+
+        % From 00h to 7h we the probability of having someone in the room is low.
+        for i = 1:420
+            P(i) = 0.001;
+        end
+        % From 7h to 8h30 we the probability rises linearly until we reach 95%.
+        for i = 421:510
+            P(i) = P(i-1) + 0.0106;
+        end
+        % The probability stays the same until 10h
+        for i = 511:600
+            P(i) = P(i-1);
+        end
+        % From 10h to 10h15 the probability decreases linearly until we reach 50%.
+        for i = 600:615
+            P(i) = P(i-1) - 0.031;
+        end
+        % From 10h15 to 10h30 the probability rises linearly until we reach 95%.
+        for i = 616:631
+            P(i) = P(i-1) + 0.031;
+        end
+        % The probability stays the same until 12h
+        for i = 632:720
+            P(i) = P(i-1);
+        end
+        % From 12h to 12h30 the probability decreases linearly until we reach 2.5%
+        for i = 720:750
+            P(i) = P(i-1) - 0.03;
+        end
+        % From 12h30 to 13h the probability stays the same
+        for i = 751:780
+            P(i) = P(i-1);
+        end
+        % From 13h to 13h30 the probability rises linearly until we reach 95%.
+        for i = 781:811
+            P(i) = P(i-1) + 0.03;
+        end
+        % From 13h30 to 15h the probability stays the same
+        for i = 812:900
+            P(i) = P(i-1);
+        end
+        % From 15h to 15h15 the probability decreases linearly until we reach 50%.
+        for i = 901:915
+            P(i) = P(i-1) - 0.031;
+        end
+        % From 15h15 to 15h30 the probability rises linearly until we reach 95%.
+        for i = 916:930
+            P(i) = P(i-1) + 0.031;
+        end
+        % The probability stays the same until 17h
+        for i = 931:1030
+            P(i) = P(i-1);
+        end
+        % From 17h to 19h the probability decreases linearly until we reach 0.7%
+        for i = 1031:1150
+            P(i) = P(i-1) - 0.0079;
+        end
+        % From 19h to 23h59 the probability decreases linearly until we reach 0.7%
+        for i = 1151:1440
+            P(i) = P(i-1);
+        end
+    
+    % repeat the one-day for 5 days
+    P_working_days = repmat(P,5,1);
+    
+    P_weekends = zeros(2*24*60,1);
+    
+    distribution_data(:,2) = [P_working_days; P_weekends];    
+    
+end
+