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Three clusters were identified by CA, and this was followed by the construction of MLR models for each cluster. Obtained statistics and MLR results are presented in Table 4 for average monthly consumption (Qm). Presented MLR models were the best-fitted ones after autocorrelation and cross-correlation analysis, and serial correlation of error term tests.

Table 4

MLR models for average monthly consumption

Dependent variableClustersExplaining componentRegression coefficientsStandard deviationp-value (F-test) p-value (Durbin‒Watson test)
Qm, average monthly consumption [l/(customer.month)] Cluster 1a (N = 18) Constant (β05.632 1.071 0.000 0.93 0.962
C−110.378 0.120
R020.001 0.000
T030.041 0.009
Cluster 2 (N = 33) Constant (β09,838.197 2,324.765 0.000 0.72 0.348
C−110.548 0.217
T−12204.274 178.492
R0341.212 14.105
T04353.398 164.221
Cluster 3 (N = 48) Constant (β04,977.236 1,203.076 0.000 0.60 0.188
C−110.400 0.110
R0210.690 3.601
T03179.221 46.468
Dependent variableClustersExplaining componentRegression coefficientsStandard deviationp-value (F-test) p-value (Durbin‒Watson test)
Qm, average monthly consumption [l/(customer.month)] Cluster 1a (N = 18) Constant (β05.632 1.071 0.000 0.93 0.962
C−110.378 0.120
R020.001 0.000
T030.041 0.009
Cluster 2 (N = 33) Constant (β09,838.197 2,324.765 0.000 0.72 0.348
C−110.548 0.217
T−12204.274 178.492
R0341.212 14.105
T04353.398 164.221
Cluster 3 (N = 48) Constant (β04,977.236 1,203.076 0.000 0.60 0.188
C−110.400 0.110
R0210.690 3.601
T03179.221 46.468

C−1 – average consumption in the previous month (L/(customer.month)); T−1 – average temperature in the previous month (°C); T0 – average temperature of the current month (°C); R0 – rainfall of the current month (mm).

aMLR model with logarithm transformation of the dependent variable (average monthly consumption).

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