14 research outputs found
Analysis of technogenic risks hazard production facilities using soft computing
A new concept has been found for the evaluation and management of risk hazard of electrical installations for a real production object on the basis of using soft computing. This approach refers to the use of managing tasks, the basis of which lies in the theory of taking decisions in indefinite conditions
Analysis of technogenic risks hazard production facilities using soft computing
A new concept has been found for the evaluation and management of risk hazard of electrical installations for a real production object on the basis of using soft computing. This approach refers to the use of managing tasks, the basis of which lies in the theory of taking decisions in indefinite conditions
Improving the accuracy of short-term load forecasting of delivery point cluster of the second level default provider
ΠΠΊΡΡΠ°Π»ΡΠ½ΠΎΡΡΡ ΠΎΠ±ΡΡΠ»ΠΎΠ²Π»Π΅Π½Π° Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎΡΡΡΡ ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΡ ΡΠΎΡΠ½ΠΎΡΡΠΈ ΠΊΡΠ°ΡΠΊΠΎΡΡΠΎΡΠ½ΠΎΠ³ΠΎ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠ»Π΅ΠΊΡΡΠΎΠΏΠΎΡΡΠ΅Π±Π»Π΅Π½ΠΈΡ Π³ΡΡΠΏΠΏ ΡΠΎΡΠ΅ΠΊ ΠΏΠΎΡΡΠ°Π²ΠΊΠΈ ΡΠ»Π΅ΠΊΡΡΠΎΡΠ½Π΅ΡΠ³ΠΈΠΈ Π³Π°ΡΠ°Π½ΡΠΈΡΡΡΡΠΈΡ
ΠΏΠΎΡΡΠ°Π²ΡΠΈΠΊΠΎΠ² Π²ΡΠΎΡΠΎΠ³ΠΎ ΡΡΠΎΠ²Π½Ρ. Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ Π΄Π°Π½Π½ΠΎΠ³ΠΎ ΠΏΡΠΎΠ³Π½ΠΎΠ·Π° ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΡΡΡΡ ΡΠΈΡΡΠ΅ΠΌΠ½ΡΠΌ ΠΎΠΏΠ΅ΡΠ°ΡΠΎΡΠΎΠΌ ΠΏΡΠΈ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΠΈ Π΄ΠΈΡΠΏΠ΅ΡΡΠ΅ΡΡΠΊΠΎΠ³ΠΎ Π³ΡΠ°ΡΠΈΠΊΠ° Π²ΡΡΠ°Π±ΠΎΡΠΊΠΈ ΠΈ ΠΏΠΎΡΡΠ΅Π±Π»Π΅Π½ΠΈΡ ΡΠ»Π΅ΠΊΡΡΠΎΡΠ½Π΅ΡΠ³ΠΈΠΈ. ΠΡΠΈΠ±ΠΊΠΈ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ, ΠΊΠ°ΠΊ ΠΏΡΠ°Π²ΠΈΠ»ΠΎ, ΠΏΡΠΈΠ²ΠΎΠ΄ΡΡ ΠΊ ΡΠ²Π΅Π»ΠΈΡΠ΅Π½ΠΈΡ ΡΠ°ΡΡ
ΠΎΠ΄Π° ΠΏΠ΅ΡΠ²ΠΈΡΠ½ΡΡ
ΡΠ½Π΅ΡΠ³ΠΎΡΠ΅ΡΡΡΡΠΎΠ² Π½Π° Π²ΡΡΠ°Π±ΠΎΡΠΊΡ ΡΠ»Π΅ΠΊΡΡΠΎΡΠ½Π΅ΡΠ³ΠΈΠΈ Π·Π° ΡΡΠ΅Ρ Π½Π΅ΠΎΠ±ΠΎΡΠ½ΠΎΠ²Π°Π½Π½ΡΡ
ΠΏΡΡΠΊΠΎΠ² ΠΈ ΠΎΡΡΠ°Π½ΠΎΠ²ΠΎΠ² Π³Π΅Π½Π΅ΡΠΈΡΡΡΡΠ΅Π³ΠΎ ΠΎΠ±ΠΎΡΡΠ΄ΠΎΠ²Π°Π½ΠΈΡ, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΊ ΡΠ²Π΅Π»ΠΈΡΠ΅Π½ΠΈΡ ΠΏΠΎΡΠ΅ΡΡ ΠΏΡΠΈ ΠΏΠ΅ΡΠ΅Π΄Π°ΡΠ΅ ΡΠ»Π΅ΠΊΡΡΠΎΡΠ½Π΅ΡΠ³ΠΈΠΈ, Π²ΡΠ»Π΅Π΄ΡΡΠ²ΠΈΠ΅ Π²ΡΠ±ΠΎΡΠ° Π½Π΅ΠΎΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΠΎΠΉ ΡΡ
Π΅ΠΌΡ ΡΠ»Π΅ΠΊΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ΅ΡΠ΅ΠΉ. Π’Π°ΠΊ ΠΊΠ°ΠΊ ΠΏΠΎΡΡΠ΅Π±Π»Π΅Π½ΠΈΠ΅ ΡΠ»Π΅ΠΊΡΡΠΎΡΠ½Π΅ΡΠ³ΠΈΠΈ Π·Π°Π²ΠΈΡΠΈΡ ΠΎΡ ΠΌΠ½ΠΎΠΆΠ΅ΡΡΠ²Π° ΡΠ°ΠΊΡΠΎΡΠΎΠ², Π·Π°Π΄Π°ΡΠ° ΠΊΡΠ°ΡΠΊΠΎΡΡΠΎΡΠ½ΠΎΠ³ΠΎ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π΄Π°Π½Π½ΠΎΠ³ΠΎ Π²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠ³ΠΎ ΡΡΠ΄Π° ΡΠ²Π»ΡΠ΅ΡΡΡ ΡΠ»Π°Π±ΠΎΡΠΎΡΠΌΠ°Π»ΠΈΠ·ΡΠ΅ΠΌΠΎΠΉ. Π Π΄Π°Π½Π½ΡΡ
ΡΡΠ»ΠΎΠ²ΠΈΡΡ
ΡΡΠ°Π΄ΠΈΡΠΈΠΎΠ½Π½ΡΠ΅ ΡΡΠ΅Π΄ΡΡΠ²Π° ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΡΠ°ΡΠΈΡΡΠΈΠΊΠΈ ΠΈ ΠΈΠΌΠΈΡΠ°ΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π½Π΅ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡ ΡΡΡΠΎΠΈΡΡ Π°Π΄Π΅ΠΊΠ²Π°ΡΠ½ΡΠ΅ ΠΏΡΠΎΠ³Π½ΠΎΠ·Π½ΡΠ΅ ΠΌΠΎΠ΄Π΅Π»ΠΈ. ΠΠΎ Π½Π΅Π΄Π°Π²Π½Π΅Π³ΠΎ Π²ΡΠ΅ΠΌΠ΅Π½ΠΈ Π΅Π΄ΠΈΠ½ΡΡΠ²Π΅Π½Π½ΡΠΌ Π°Π΄Π΅ΠΊΠ²Π°ΡΠ½ΡΠΌ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠΌ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΏΠΎΡΡΠ΅Π±Π»Π΅Π½ΠΈΡ ΡΠ»Π΅ΠΊΡΡΠΎΡΠ½Π΅ΡΠ³ΠΈΠΈ Π±ΡΠ» ΠΌΠ΅ΡΠΎΠ΄ ΡΠΊΡΠΏΠ΅ΡΡΠ½ΡΡ
ΠΎΡΠ΅Π½ΠΎΠΊ. Π Π½Π°ΡΡΠΎΡΡΠ΅Π΅ Π²ΡΠ΅ΠΌΡ Π΄Π»Ρ ΠΊΡΠ°ΡΠΊΠΎΡΡΠΎΡΠ½ΠΎΠ³ΠΎ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠ»Π΅ΠΊΡΡΠΎΠΏΠΎΡΡΠ΅Π±Π»Π΅Π½ΠΈΡ ΡΠ½Π΅ΡΠ³ΠΎΡΠΈΡΡΠ΅ΠΌΡ ΡΠ΅Π³ΠΈΠΎΠ½Π° ΡΡΡΠ°Π½Ρ ΠΈΠ»ΠΈ Π³ΡΡΠΏΠΏΡ ΡΠΎΡΠ΅ΠΊ ΠΏΠΎΡΡΠ°Π²ΠΊΠΈ ΡΠ»Π΅ΠΊΡΡΠΎΡΠ½Π΅ΡΠ³ΠΈΠΈ Π³Π°ΡΠ°Π½ΡΠΈΡΡΡΡΠ΅Π³ΠΎ ΠΏΠΎΡΡΠ°Π²ΡΠΈΠΊΠ° ΠΏΠ΅ΡΠ²ΠΎΠ³ΠΎ ΡΡΠΎΠ²Π½Ρ Π²ΡΠ΅ Π±ΠΎΠ»ΡΡΠ΅Π΅ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ Π½Π°Ρ
ΠΎΠ΄ΡΡ ΠΈΠ½ΡΡΡΡΠΌΠ΅Π½ΡΡ Π½Π΅ΠΉΡΠΎΠ½Π½ΡΡ
ΡΠ΅ΡΠ΅ΠΉ. ΠΠ΄Π½Π°ΠΊΠΎ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π½ΡΠ΅ ΠΌΠΎΠ΄Π΅Π»ΠΈ ΠΌΠ°Π»ΠΎ ΠΏΡΠΈΠ³ΠΎΠ΄Π½Ρ Π΄Π»Ρ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΏΠΎΡΠ°ΡΠΎΠ²ΠΎΠ³ΠΎ ΠΏΠΎΡΡΠ΅Π±Π»Π΅Π½ΠΈΡ ΡΠ»Π΅ΠΊΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠ½Π΅ΡΠ³ΠΈΠΈ Π³ΡΡΠΏΠΏ ΡΠΎΡΠ΅ΠΊ ΠΏΠΎΡΡΠ°Π²ΠΊΠΈ ΡΠ»Π΅ΠΊΡΡΠΎΡΠ½Π΅ΡΠ³ΠΈΠΈ Π³Π°ΡΠ°Π½ΡΠΈΡΡΡΡΠΈΡ
ΠΏΠΎΡΡΠ°Π²ΡΠΈΠΊΠΎΠ² Π²ΡΠΎΡΠΎΠ³ΠΎ ΡΡΠΎΠ²Π½Ρ. ΠΡΠ°ΡΠΊΠΎΡΡΠΎΡΠ½ΠΎΠ΅ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΠΏΠΎΡΡΠ΅Π±Π»Π΅Π½ΠΈΡ ΡΠ»Π΅ΠΊΡΡΠΎΡΠ½Π΅ΡΠ³ΠΈΠΈ Π΄Π°Π½Π½ΡΡ
ΠΎΠ±ΡΠ΅ΠΊΡΠΎΠ² ΠΎΡΠ»ΠΎΠΆΠ½Π΅Π½ΠΎ ΡΠ΅ΠΌ, ΡΡΠΎ, ΠΏΠΎΠΌΠΈΠΌΠΎ ΡΡΠ°Π½Π΄Π°ΡΡΠ½ΡΡ
Π²ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ
ΠΈ ΠΌΠ΅ΡΠ΅ΠΎΡΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ°ΠΊΡΠΎΡΠΎΠ², Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎ ΡΡΠΈΡΡΠ²Π°ΡΡ Π½Π°Π΄Π΅ΠΆΠ½ΠΎΡΡΡ ΡΠ»Π΅ΠΊΡΡΠΎΡΠ΅ΡΠ΅Π²ΠΎΠ³ΠΎ ΠΎΠ±ΠΎΡΡΠ΄ΠΎΠ²Π°Π½ΠΈΡ 6-110 ΠΊΠ, ΡΠ΅ΠΆΠΈΠΌ ΡΠ°Π±ΠΎΡΡ ΠΏΠΎΡΡΠ΅Π±ΠΈΡΠ΅Π»Π΅ΠΉ ΡΠ»Π΅ΠΊΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠ½Π΅ΡΠ³ΠΈΠΈ Ρ ΠΌΠΎΡΠ½ΠΎΡΡΡΡ 670-10000 ΠΊΠΡ, Π° ΡΠ°ΠΊΠΆΠ΅ Π½Π°Π»ΠΈΡΠΈΠ΅ ΡΠ΅Π½ΡΡΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΎΡΠΎΠΏΠ»Π΅Π½ΠΈΡ ΠΈ Π³ΠΎΡΡΡΠ΅Π³ΠΎ (Ρ
ΠΎΠ»ΠΎΠ΄Π½ΠΎΠ³ΠΎ) Π²ΠΎΠ΄ΠΎΡΠ½Π°Π±ΠΆΠ΅Π½ΠΈΡ Π² Π½Π°ΡΠ΅Π»Π΅Π½Π½ΠΎΠΌ ΠΏΡΠ½ΠΊΡΠ΅, ΠΏΠΈΡΠ°ΡΡΠ΅ΠΌΡΡ ΠΎΡ Π³ΡΡΠΏΠΏΡ ΡΠΎΡΠ΅ΠΊ ΠΏΠΎΡΡΠ°Π²ΠΊΠΈ ΡΠ»Π΅ΠΊΡΡΠΎΡΠ½Π΅ΡΠ³ΠΈΠΈ. ΠΠ»Ρ Π½ΠΈΡ
ΠΎΡΡΠ°Π΅ΡΡΡ ΠΎΡΠΊΡΡΡΡΠΌ Π²ΠΎΠΏΡΠΎΡ Π²ΡΠ±ΠΎΡΠ° ΠΎΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΠΎΠΉ Π°ΡΡ
ΠΈΡΠ΅ΠΊΡΡΡΡ ΠΈ ΠΊΠΎΠ½ΡΠΈΠ³ΡΡΠ°ΡΠΈΠΈ Π½Π΅ΠΉΡΠΎΡΠ΅ΡΠ΅Π²ΠΎΠΉ ΠΌΠΎΠ΄Π΅Π»ΠΈ, Π° ΡΠ°ΠΊΠΆΠ΅ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° Π΅Π΅ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ, ΠΏΡΠΈ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠΈ ΠΊΠΎΡΠΎΡΡΡ
Π΄ΠΎΡΡΠΈΠ³Π°Π΅ΡΡΡ ΡΡΠ΅Π±ΡΠ΅ΠΌΠ°Ρ ΡΠΎΡΠ½ΠΎΡΡΡ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ. Π¦Π΅Π»Ρ: ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΠ΅ ΡΠΎΡΠ½ΠΎΡΡΠΈ ΠΊΡΠ°ΡΠΊΠΎΡΡΠΎΡΠ½ΠΎΠ³ΠΎ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠ»Π΅ΠΊΡΡΠΎΠΏΠΎΡΡΠ΅Π±Π»Π΅Π½ΠΈΡ Π³ΡΡΠΏΠΏ ΡΠΎΡΠ΅ΠΊ ΠΏΠΎΡΡΠ°Π²ΠΊΠΈ ΡΠ»Π΅ΠΊΡΡΠΎΡΠ½Π΅ΡΠ³ΠΈΠΈ Π³Π°ΡΠ°Π½ΡΠΈΡΡΡΡΠ΅Π³ΠΎ ΠΏΠΎΡΡΠ°Π²ΡΠΈΠΊΠ° Π²ΡΠΎΡΠΎΠ³ΠΎ ΡΡΠΎΠ²Π½Ρ Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΠΈΠ½ΡΡΡΡΠΌΠ΅Π½ΡΠΎΠ² Π½Π΅ΠΉΡΠΎΠ½Π½ΡΡ
ΡΠ΅ΡΠ΅ΠΉ ΠΈ Π³Π»ΡΠ±ΠΎΠΊΠΎΠ³ΠΎ ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ³ΠΎ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ. ΠΠ΅ΡΠΎΠ΄Ρ: ΠΌΠ΅ΡΠΎΠ΄Ρ ΠΊΠΎΡΡΠ΅Π»ΡΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ ΠΈ ΡΠ°ΠΊΡΠΎΡΠ½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π°, ΡΠ΅ΠΎΡΠΈΠΈ ΠΈΡΠΊΡΡΡΡΠ²Π΅Π½Π½ΡΡ
Π½Π΅ΠΉΡΠΎΠ½Π½ΡΡ
ΡΠ΅ΡΠ΅ΠΉ ΠΈ ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ³ΠΎ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ. ΠΡΠΎΠ³ΡΠ°ΠΌΠΌΠ½Π°Ρ ΡΠ΅Π°Π»ΠΈΠ·Π°ΡΠΈΡ ΡΠ΅ΠΎΡΠ΅ΡΠΈΡΠ΅ΡΠΊΠΈΡ
Π²ΡΠΊΠ»Π°Π΄ΠΎΠΊ Π²ΡΠΏΠΎΠ»Π½Π΅Π½Π° Ρ ΠΏΠΎΠΌΠΎΡΡΡ Π±ΠΈΠ±Π»ΠΈΠΎΡΠ΅ΠΊΠΈ Π³Π»ΡΠ±ΠΎΠΊΠΎΠ³ΠΎ ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ³ΠΎ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ Tensor flow Keras Π½Π° ΡΠ·ΡΠΊΠ΅ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ Python 3.6. Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ. Π Π°Π·ΡΠ°Π±ΠΎΡΠ°Π½ Π½Π΅ΠΉΡΠΎΡΠ΅ΡΠ΅Π²ΠΎΠΉ Π°Π»Π³ΠΎΡΠΈΡΠΌ ΠΊΡΠ°ΡΠΊΠΎΡΡΠΎΡΠ½ΠΎΠ³ΠΎ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠ»Π΅ΠΊΡΡΠΎΠΏΠΎΡΡΠ΅Π±Π»Π΅Π½ΠΈΡ Π³ΡΡΠΏΠΏ ΡΠΎΡΠ΅ΠΊ ΠΏΠΎΡΡΠ°Π²ΠΊΠΈ ΡΠ»Π΅ΠΊΡΡΠΎΡΠ½Π΅ΡΠ³ΠΈΠΈ Π³Π°ΡΠ°Π½ΡΠΈΡΡΡΡΠ΅Π³ΠΎ ΠΏΠΎΡΡΠ°Π²ΡΠΈΠΊΠ° Π²ΡΠΎΡΠΎΠ³ΠΎ ΡΡΠΎΠ²Π½Ρ Ρ Π°Π΄Π°ΠΏΡΠΈΠ²Π½ΡΠΌΠΈ Π² ΠΏΡΠΎΡΠ΅ΡΡΠ΅ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠ°ΠΌΠΈ ΡΠΊΠΎΡΠΎΡΡΠΈ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ ΠΈ ΠΌΠΎΠΌΠ΅Π½ΡΠ° ΠΈΠ½Π΅ΡΡΠΈΠΈ. ΠΡΠΏΠΎΠ»Π½Π΅Π½Π° ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ½Π°Ρ ΡΠ΅Π°Π»ΠΈΠ·Π°ΡΠΈΡ Π΄Π°Π½Π½ΠΎΠ³ΠΎ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° Π² Π±ΠΈΠ±Π»ΠΈΠΎΡΠ΅ΠΊΠ΅ Π³Π»ΡΠ±ΠΎΠΊΠΎΠ³ΠΎ ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ³ΠΎ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ Tensor flow Keras. ΠΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ Π΄Π°Π½Π½ΠΎΠΉ ΠΈΡΠΊΡΡΡΡΠ²Π΅Π½Π½ΠΎΠΉ Π½Π΅ΠΉΡΠΎΠ½Π½ΠΎΠΉ ΡΠ΅ΡΠΈ ΠΏΠΎΠ·Π²ΠΎΠ»ΠΈΠ»ΠΎ ΡΠ½ΠΈΠ·ΠΈΡΡ ΡΡΠ΅Π΄Π½Π΅ΠΌΠ΅ΡΡΡΠ½ΡΡ ΠΎΡΠ½ΠΎΡΠΈΡΠ΅Π»ΡΠ½ΡΡ ΠΎΡΠΈΠ±ΠΊΡ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π½Π° 5,14 %.Relevance of the discussed issue is caused by the need to improve the accuracy of short-term load forecasting of delivery point cluster of the second level default provider. The system operator uses the result of forecast when forming power system dispatch load curve. Usually, prediction errors lead to increase of primary energy resources consumption for electric-power production, due to unjustified run and shutdown of generating equipment, as well as increasing of circuit losses, due to the choice of non-optimal scheme of electric grid. As the electricity consumption depends on many factors, the task of short-term load forecasting is poorly formalized. Under these conditions, traditional methods of mathematical statistics and simulation do not allow building the adequate forecast models. Until recently, the only fine method of load forecasting was the Delhi approach. Currently, tools of neural networks and deep machine learning are widely used for short-term load forecasting of the energy system of a region of the country or delivery point cluster of first level default provider. However, the developed models are not suitable for predicting hourly electricity consumption of delivery point cluster of the second level default provider. Short-term load forecasting of this object is complicated of reliability of electric grid 6-110 kV, the operating mode of electricity consumers with a capacity of 670-10000 kW, the presence of district heating and water supply, beside standard time and meteorological factors. For this forecasting object, the question of choosing the optimal architecture and configuration of the neural network model, as well as the learning algorithm, which can achieve the desired forecasting accuracy, remain open. The main aim of the research is to improve the accuracy of short-term load forecasting of delivery point cluster of the second level default provider with the help of tools of neural networks and deep machine learning. The methods: the methods of correlation and factor analysis, the theory of artificial neural networks and machine learning. Software implementation of theoretical calculations was performed with help of deep machine learning library Tensor flow Keras in the Python 3.6 programming language. Results. The authors have developed the neural network algorithm for short-term load forecasting of delivery point cluster of the second level default provider with adaptive learning and momentum rate and completed the software implementation of this algorithm in deep machine learning library Tensor flow Keras. The use of this artificial neural network let to decrease in monthly average relative forecast error by 5,14 %
Improving the accuracy of short-term load forecasting of delivery point cluster of the second level default provider
ΠΠΊΡΡΠ°Π»ΡΠ½ΠΎΡΡΡ ΠΎΠ±ΡΡΠ»ΠΎΠ²Π»Π΅Π½Π° Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎΡΡΡΡ ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΡ ΡΠΎΡΠ½ΠΎΡΡΠΈ ΠΊΡΠ°ΡΠΊΠΎΡΡΠΎΡΠ½ΠΎΠ³ΠΎ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠ»Π΅ΠΊΡΡΠΎΠΏΠΎΡΡΠ΅Π±Π»Π΅Π½ΠΈΡ Π³ΡΡΠΏΠΏ ΡΠΎΡΠ΅ΠΊ ΠΏΠΎΡΡΠ°Π²ΠΊΠΈ ΡΠ»Π΅ΠΊΡΡΠΎΡΠ½Π΅ΡΠ³ΠΈΠΈ Π³Π°ΡΠ°Π½ΡΠΈΡΡΡΡΠΈΡ
ΠΏΠΎΡΡΠ°Π²ΡΠΈΠΊΠΎΠ² Π²ΡΠΎΡΠΎΠ³ΠΎ ΡΡΠΎΠ²Π½Ρ. Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ Π΄Π°Π½Π½ΠΎΠ³ΠΎ ΠΏΡΠΎΠ³Π½ΠΎΠ·Π° ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΡΡΡΡ ΡΠΈΡΡΠ΅ΠΌΠ½ΡΠΌ ΠΎΠΏΠ΅ΡΠ°ΡΠΎΡΠΎΠΌ ΠΏΡΠΈ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΠΈ Π΄ΠΈΡΠΏΠ΅ΡΡΠ΅ΡΡΠΊΠΎΠ³ΠΎ Π³ΡΠ°ΡΠΈΠΊΠ° Π²ΡΡΠ°Π±ΠΎΡΠΊΠΈ ΠΈ ΠΏΠΎΡΡΠ΅Π±Π»Π΅Π½ΠΈΡ ΡΠ»Π΅ΠΊΡΡΠΎΡΠ½Π΅ΡΠ³ΠΈΠΈ. ΠΡΠΈΠ±ΠΊΠΈ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ, ΠΊΠ°ΠΊ ΠΏΡΠ°Π²ΠΈΠ»ΠΎ, ΠΏΡΠΈΠ²ΠΎΠ΄ΡΡ ΠΊ ΡΠ²Π΅Π»ΠΈΡΠ΅Π½ΠΈΡ ΡΠ°ΡΡ
ΠΎΠ΄Π° ΠΏΠ΅ΡΠ²ΠΈΡΠ½ΡΡ
ΡΠ½Π΅ΡΠ³ΠΎΡΠ΅ΡΡΡΡΠΎΠ² Π½Π° Π²ΡΡΠ°Π±ΠΎΡΠΊΡ ΡΠ»Π΅ΠΊΡΡΠΎΡΠ½Π΅ΡΠ³ΠΈΠΈ Π·Π° ΡΡΠ΅Ρ Π½Π΅ΠΎΠ±ΠΎΡΠ½ΠΎΠ²Π°Π½Π½ΡΡ
ΠΏΡΡΠΊΠΎΠ² ΠΈ ΠΎΡΡΠ°Π½ΠΎΠ²ΠΎΠ² Π³Π΅Π½Π΅ΡΠΈΡΡΡΡΠ΅Π³ΠΎ ΠΎΠ±ΠΎΡΡΠ΄ΠΎΠ²Π°Π½ΠΈΡ, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΊ ΡΠ²Π΅Π»ΠΈΡΠ΅Π½ΠΈΡ ΠΏΠΎΡΠ΅ΡΡ ΠΏΡΠΈ ΠΏΠ΅ΡΠ΅Π΄Π°ΡΠ΅ ΡΠ»Π΅ΠΊΡΡΠΎΡΠ½Π΅ΡΠ³ΠΈΠΈ, Π²ΡΠ»Π΅Π΄ΡΡΠ²ΠΈΠ΅ Π²ΡΠ±ΠΎΡΠ° Π½Π΅ΠΎΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΠΎΠΉ ΡΡ
Π΅ΠΌΡ ΡΠ»Π΅ΠΊΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ΅ΡΠ΅ΠΉ. Π’Π°ΠΊ ΠΊΠ°ΠΊ ΠΏΠΎΡΡΠ΅Π±Π»Π΅Π½ΠΈΠ΅ ΡΠ»Π΅ΠΊΡΡΠΎΡΠ½Π΅ΡΠ³ΠΈΠΈ Π·Π°Π²ΠΈΡΠΈΡ ΠΎΡ ΠΌΠ½ΠΎΠΆΠ΅ΡΡΠ²Π° ΡΠ°ΠΊΡΠΎΡΠΎΠ², Π·Π°Π΄Π°ΡΠ° ΠΊΡΠ°ΡΠΊΠΎΡΡΠΎΡΠ½ΠΎΠ³ΠΎ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π΄Π°Π½Π½ΠΎΠ³ΠΎ Π²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠ³ΠΎ ΡΡΠ΄Π° ΡΠ²Π»ΡΠ΅ΡΡΡ ΡΠ»Π°Π±ΠΎΡΠΎΡΠΌΠ°Π»ΠΈΠ·ΡΠ΅ΠΌΠΎΠΉ. Π Π΄Π°Π½Π½ΡΡ
ΡΡΠ»ΠΎΠ²ΠΈΡΡ
ΡΡΠ°Π΄ΠΈΡΠΈΠΎΠ½Π½ΡΠ΅ ΡΡΠ΅Π΄ΡΡΠ²Π° ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΡΠ°ΡΠΈΡΡΠΈΠΊΠΈ ΠΈ ΠΈΠΌΠΈΡΠ°ΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π½Π΅ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡ ΡΡΡΠΎΠΈΡΡ Π°Π΄Π΅ΠΊΠ²Π°ΡΠ½ΡΠ΅ ΠΏΡΠΎΠ³Π½ΠΎΠ·Π½ΡΠ΅ ΠΌΠΎΠ΄Π΅Π»ΠΈ. ΠΠΎ Π½Π΅Π΄Π°Π²Π½Π΅Π³ΠΎ Π²ΡΠ΅ΠΌΠ΅Π½ΠΈ Π΅Π΄ΠΈΠ½ΡΡΠ²Π΅Π½Π½ΡΠΌ Π°Π΄Π΅ΠΊΠ²Π°ΡΠ½ΡΠΌ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠΌ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΏΠΎΡΡΠ΅Π±Π»Π΅Π½ΠΈΡ ΡΠ»Π΅ΠΊΡΡΠΎΡΠ½Π΅ΡΠ³ΠΈΠΈ Π±ΡΠ» ΠΌΠ΅ΡΠΎΠ΄ ΡΠΊΡΠΏΠ΅ΡΡΠ½ΡΡ
ΠΎΡΠ΅Π½ΠΎΠΊ. Π Π½Π°ΡΡΠΎΡΡΠ΅Π΅ Π²ΡΠ΅ΠΌΡ Π΄Π»Ρ ΠΊΡΠ°ΡΠΊΠΎΡΡΠΎΡΠ½ΠΎΠ³ΠΎ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠ»Π΅ΠΊΡΡΠΎΠΏΠΎΡΡΠ΅Π±Π»Π΅Π½ΠΈΡ ΡΠ½Π΅ΡΠ³ΠΎΡΠΈΡΡΠ΅ΠΌΡ ΡΠ΅Π³ΠΈΠΎΠ½Π° ΡΡΡΠ°Π½Ρ ΠΈΠ»ΠΈ Π³ΡΡΠΏΠΏΡ ΡΠΎΡΠ΅ΠΊ ΠΏΠΎΡΡΠ°Π²ΠΊΠΈ ΡΠ»Π΅ΠΊΡΡΠΎΡΠ½Π΅ΡΠ³ΠΈΠΈ Π³Π°ΡΠ°Π½ΡΠΈΡΡΡΡΠ΅Π³ΠΎ ΠΏΠΎΡΡΠ°Π²ΡΠΈΠΊΠ° ΠΏΠ΅ΡΠ²ΠΎΠ³ΠΎ ΡΡΠΎΠ²Π½Ρ Π²ΡΠ΅ Π±ΠΎΠ»ΡΡΠ΅Π΅ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ Π½Π°Ρ
ΠΎΠ΄ΡΡ ΠΈΠ½ΡΡΡΡΠΌΠ΅Π½ΡΡ Π½Π΅ΠΉΡΠΎΠ½Π½ΡΡ
ΡΠ΅ΡΠ΅ΠΉ. ΠΠ΄Π½Π°ΠΊΠΎ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π½ΡΠ΅ ΠΌΠΎΠ΄Π΅Π»ΠΈ ΠΌΠ°Π»ΠΎ ΠΏΡΠΈΠ³ΠΎΠ΄Π½Ρ Π΄Π»Ρ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΏΠΎΡΠ°ΡΠΎΠ²ΠΎΠ³ΠΎ ΠΏΠΎΡΡΠ΅Π±Π»Π΅Π½ΠΈΡ ΡΠ»Π΅ΠΊΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠ½Π΅ΡΠ³ΠΈΠΈ Π³ΡΡΠΏΠΏ ΡΠΎΡΠ΅ΠΊ ΠΏΠΎΡΡΠ°Π²ΠΊΠΈ ΡΠ»Π΅ΠΊΡΡΠΎΡΠ½Π΅ΡΠ³ΠΈΠΈ Π³Π°ΡΠ°Π½ΡΠΈΡΡΡΡΠΈΡ
ΠΏΠΎΡΡΠ°Π²ΡΠΈΠΊΠΎΠ² Π²ΡΠΎΡΠΎΠ³ΠΎ ΡΡΠΎΠ²Π½Ρ. ΠΡΠ°ΡΠΊΠΎΡΡΠΎΡΠ½ΠΎΠ΅ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΠΏΠΎΡΡΠ΅Π±Π»Π΅Π½ΠΈΡ ΡΠ»Π΅ΠΊΡΡΠΎΡΠ½Π΅ΡΠ³ΠΈΠΈ Π΄Π°Π½Π½ΡΡ
ΠΎΠ±ΡΠ΅ΠΊΡΠΎΠ² ΠΎΡΠ»ΠΎΠΆΠ½Π΅Π½ΠΎ ΡΠ΅ΠΌ, ΡΡΠΎ, ΠΏΠΎΠΌΠΈΠΌΠΎ ΡΡΠ°Π½Π΄Π°ΡΡΠ½ΡΡ
Π²ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ
ΠΈ ΠΌΠ΅ΡΠ΅ΠΎΡΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ°ΠΊΡΠΎΡΠΎΠ², Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎ ΡΡΠΈΡΡΠ²Π°ΡΡ Π½Π°Π΄Π΅ΠΆΠ½ΠΎΡΡΡ ΡΠ»Π΅ΠΊΡΡΠΎΡΠ΅ΡΠ΅Π²ΠΎΠ³ΠΎ ΠΎΠ±ΠΎΡΡΠ΄ΠΎΠ²Π°Π½ΠΈΡ 6-110 ΠΊΠ, ΡΠ΅ΠΆΠΈΠΌ ΡΠ°Π±ΠΎΡΡ ΠΏΠΎΡΡΠ΅Π±ΠΈΡΠ΅Π»Π΅ΠΉ ΡΠ»Π΅ΠΊΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠ½Π΅ΡΠ³ΠΈΠΈ Ρ ΠΌΠΎΡΠ½ΠΎΡΡΡΡ 670-10000 ΠΊΠΡ, Π° ΡΠ°ΠΊΠΆΠ΅ Π½Π°Π»ΠΈΡΠΈΠ΅ ΡΠ΅Π½ΡΡΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΎΡΠΎΠΏΠ»Π΅Π½ΠΈΡ ΠΈ Π³ΠΎΡΡΡΠ΅Π³ΠΎ (Ρ
ΠΎΠ»ΠΎΠ΄Π½ΠΎΠ³ΠΎ) Π²ΠΎΠ΄ΠΎΡΠ½Π°Π±ΠΆΠ΅Π½ΠΈΡ Π² Π½Π°ΡΠ΅Π»Π΅Π½Π½ΠΎΠΌ ΠΏΡΠ½ΠΊΡΠ΅, ΠΏΠΈΡΠ°ΡΡΠ΅ΠΌΡΡ ΠΎΡ Π³ΡΡΠΏΠΏΡ ΡΠΎΡΠ΅ΠΊ ΠΏΠΎΡΡΠ°Π²ΠΊΠΈ ΡΠ»Π΅ΠΊΡΡΠΎΡΠ½Π΅ΡΠ³ΠΈΠΈ. ΠΠ»Ρ Π½ΠΈΡ
ΠΎΡΡΠ°Π΅ΡΡΡ ΠΎΡΠΊΡΡΡΡΠΌ Π²ΠΎΠΏΡΠΎΡ Π²ΡΠ±ΠΎΡΠ° ΠΎΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΠΎΠΉ Π°ΡΡ
ΠΈΡΠ΅ΠΊΡΡΡΡ ΠΈ ΠΊΠΎΠ½ΡΠΈΠ³ΡΡΠ°ΡΠΈΠΈ Π½Π΅ΠΉΡΠΎΡΠ΅ΡΠ΅Π²ΠΎΠΉ ΠΌΠΎΠ΄Π΅Π»ΠΈ, Π° ΡΠ°ΠΊΠΆΠ΅ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° Π΅Π΅ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ, ΠΏΡΠΈ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠΈ ΠΊΠΎΡΠΎΡΡΡ
Π΄ΠΎΡΡΠΈΠ³Π°Π΅ΡΡΡ ΡΡΠ΅Π±ΡΠ΅ΠΌΠ°Ρ ΡΠΎΡΠ½ΠΎΡΡΡ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ. Π¦Π΅Π»Ρ: ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΠ΅ ΡΠΎΡΠ½ΠΎΡΡΠΈ ΠΊΡΠ°ΡΠΊΠΎΡΡΠΎΡΠ½ΠΎΠ³ΠΎ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠ»Π΅ΠΊΡΡΠΎΠΏΠΎΡΡΠ΅Π±Π»Π΅Π½ΠΈΡ Π³ΡΡΠΏΠΏ ΡΠΎΡΠ΅ΠΊ ΠΏΠΎΡΡΠ°Π²ΠΊΠΈ ΡΠ»Π΅ΠΊΡΡΠΎΡΠ½Π΅ΡΠ³ΠΈΠΈ Π³Π°ΡΠ°Π½ΡΠΈΡΡΡΡΠ΅Π³ΠΎ ΠΏΠΎΡΡΠ°Π²ΡΠΈΠΊΠ° Π²ΡΠΎΡΠΎΠ³ΠΎ ΡΡΠΎΠ²Π½Ρ Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΠΈΠ½ΡΡΡΡΠΌΠ΅Π½ΡΠΎΠ² Π½Π΅ΠΉΡΠΎΠ½Π½ΡΡ
ΡΠ΅ΡΠ΅ΠΉ ΠΈ Π³Π»ΡΠ±ΠΎΠΊΠΎΠ³ΠΎ ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ³ΠΎ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ. ΠΠ΅ΡΠΎΠ΄Ρ: ΠΌΠ΅ΡΠΎΠ΄Ρ ΠΊΠΎΡΡΠ΅Π»ΡΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ ΠΈ ΡΠ°ΠΊΡΠΎΡΠ½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π°, ΡΠ΅ΠΎΡΠΈΠΈ ΠΈΡΠΊΡΡΡΡΠ²Π΅Π½Π½ΡΡ
Π½Π΅ΠΉΡΠΎΠ½Π½ΡΡ
ΡΠ΅ΡΠ΅ΠΉ ΠΈ ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ³ΠΎ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ. ΠΡΠΎΠ³ΡΠ°ΠΌΠΌΠ½Π°Ρ ΡΠ΅Π°Π»ΠΈΠ·Π°ΡΠΈΡ ΡΠ΅ΠΎΡΠ΅ΡΠΈΡΠ΅ΡΠΊΠΈΡ
Π²ΡΠΊΠ»Π°Π΄ΠΎΠΊ Π²ΡΠΏΠΎΠ»Π½Π΅Π½Π° Ρ ΠΏΠΎΠΌΠΎΡΡΡ Π±ΠΈΠ±Π»ΠΈΠΎΡΠ΅ΠΊΠΈ Π³Π»ΡΠ±ΠΎΠΊΠΎΠ³ΠΎ ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ³ΠΎ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ Tensor flow Keras Π½Π° ΡΠ·ΡΠΊΠ΅ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ Python 3.6. Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ. Π Π°Π·ΡΠ°Π±ΠΎΡΠ°Π½ Π½Π΅ΠΉΡΠΎΡΠ΅ΡΠ΅Π²ΠΎΠΉ Π°Π»Π³ΠΎΡΠΈΡΠΌ ΠΊΡΠ°ΡΠΊΠΎΡΡΠΎΡΠ½ΠΎΠ³ΠΎ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠ»Π΅ΠΊΡΡΠΎΠΏΠΎΡΡΠ΅Π±Π»Π΅Π½ΠΈΡ Π³ΡΡΠΏΠΏ ΡΠΎΡΠ΅ΠΊ ΠΏΠΎΡΡΠ°Π²ΠΊΠΈ ΡΠ»Π΅ΠΊΡΡΠΎΡΠ½Π΅ΡΠ³ΠΈΠΈ Π³Π°ΡΠ°Π½ΡΠΈΡΡΡΡΠ΅Π³ΠΎ ΠΏΠΎΡΡΠ°Π²ΡΠΈΠΊΠ° Π²ΡΠΎΡΠΎΠ³ΠΎ ΡΡΠΎΠ²Π½Ρ Ρ Π°Π΄Π°ΠΏΡΠΈΠ²Π½ΡΠΌΠΈ Π² ΠΏΡΠΎΡΠ΅ΡΡΠ΅ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠ°ΠΌΠΈ ΡΠΊΠΎΡΠΎΡΡΠΈ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ ΠΈ ΠΌΠΎΠΌΠ΅Π½ΡΠ° ΠΈΠ½Π΅ΡΡΠΈΠΈ. ΠΡΠΏΠΎΠ»Π½Π΅Π½Π° ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ½Π°Ρ ΡΠ΅Π°Π»ΠΈΠ·Π°ΡΠΈΡ Π΄Π°Π½Π½ΠΎΠ³ΠΎ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° Π² Π±ΠΈΠ±Π»ΠΈΠΎΡΠ΅ΠΊΠ΅ Π³Π»ΡΠ±ΠΎΠΊΠΎΠ³ΠΎ ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ³ΠΎ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ Tensor flow Keras. ΠΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ Π΄Π°Π½Π½ΠΎΠΉ ΠΈΡΠΊΡΡΡΡΠ²Π΅Π½Π½ΠΎΠΉ Π½Π΅ΠΉΡΠΎΠ½Π½ΠΎΠΉ ΡΠ΅ΡΠΈ ΠΏΠΎΠ·Π²ΠΎΠ»ΠΈΠ»ΠΎ ΡΠ½ΠΈΠ·ΠΈΡΡ ΡΡΠ΅Π΄Π½Π΅ΠΌΠ΅ΡΡΡΠ½ΡΡ ΠΎΡΠ½ΠΎΡΠΈΡΠ΅Π»ΡΠ½ΡΡ ΠΎΡΠΈΠ±ΠΊΡ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π½Π° 5,14 %.Relevance of the discussed issue is caused by the need to improve the accuracy of short-term load forecasting of delivery point cluster of the second level default provider. The system operator uses the result of forecast when forming power system dispatch load curve. Usually, prediction errors lead to increase of primary energy resources consumption for electric-power production, due to unjustified run and shutdown of generating equipment, as well as increasing of circuit losses, due to the choice of non-optimal scheme of electric grid. As the electricity consumption depends on many factors, the task of short-term load forecasting is poorly formalized. Under these conditions, traditional methods of mathematical statistics and simulation do not allow building the adequate forecast models. Until recently, the only fine method of load forecasting was the Delhi approach. Currently, tools of neural networks and deep machine learning are widely used for short-term load forecasting of the energy system of a region of the country or delivery point cluster of first level default provider. However, the developed models are not suitable for predicting hourly electricity consumption of delivery point cluster of the second level default provider. Short-term load forecasting of this object is complicated of reliability of electric grid 6-110 kV, the operating mode of electricity consumers with a capacity of 670-10000 kW, the presence of district heating and water supply, beside standard time and meteorological factors. For this forecasting object, the question of choosing the optimal architecture and configuration of the neural network model, as well as the learning algorithm, which can achieve the desired forecasting accuracy, remain open. The main aim of the research is to improve the accuracy of short-term load forecasting of delivery point cluster of the second level default provider with the help of tools of neural networks and deep machine learning. The methods: the methods of correlation and factor analysis, the theory of artificial neural networks and machine learning. Software implementation of theoretical calculations was performed with help of deep machine learning library Tensor flow Keras in the Python 3.6 programming language. Results. The authors have developed the neural network algorithm for short-term load forecasting of delivery point cluster of the second level default provider with adaptive learning and momentum rate and completed the software implementation of this algorithm in deep machine learning library Tensor flow Keras. The use of this artificial neural network let to decrease in monthly average relative forecast error by 5,14 %
Controlling the regioselectivity and stereospecificity of FAD-dependent polyamine oxidases with the use of amine-attached guide molecules as conformational modulators
Accepted Manuscript201