PINN-DT: Haɗaɗɗun Cibiyoyin Jijiyoyin Lantarki Masu Ƙididdigar Fizik don Haɓaka Makamashi a Gine-ginen Wayo

Teburin Abubuwan Ciki

1. Gabatarwa

Haɗa fasahohin wayoyin lantarki na wayo tare da ingantattun hanyoyin lissafi yana da muhimmanci don magance matsalar makamashi ta duniya. Gine-gine suna ɗaukar kusan kashi 30% na jimillar amfani da makamashi a {Amurka}, tare da manyan kayan aiki masu amfani da makamashi kamar na'urorin wanki da na'urorin sanyaya iska. Tsarin Gudanar da Makamashi na Gida na Al'ada (HEMS) yana fuskantar iyakancewa a cikin sarƙaƙƙiyar lissafi da kuma sarrafa rashin tabbas a cikin halayen mai amfani da wadatar makamashi.

Tsarin PINN-DT da aka tsara yana magance waɗannan ƙalubalen ta hanyar haɗaɗɗun hanyoyin haɗa Koyon Ƙarfafa Mai zurfi (DRL), Cibiyoyin Jijiyoyin Lantarki Masu Ƙididdigar Fizik (PINNs), da fasahar Blockchain. Wannan haɗin yana ba da damar ingantaccen amfani da makamashi cikin sauri yayin tabbatar da ingantaccen tsari, fahimta, da tsaro a cikin kayan aikin wayar lantarki na wayo.

2. Hanyar Aiki

2.1 Cibiyoyin Jijiyoyin Lantarki Masu Ƙididdigar Fizik (PINNs)

PINNs sun haɗa dokokin fizikai kai tsaye cikin tsarin horar da cibiyoyin jijiyoyin lantarki, suna tabbatar da cewa hasashe sun bi ka'idojin fizikai na asali. Ayyukan asara sun haɗa kalmomin da aka samo daga bayanai tare da ƙayyadaddun tushen fiziki:

$\mathcal{L}_{total} = \mathcal{L}_{data} + \lambda \mathcal{L}_{physics}$

Inda $\mathcal{L}_{data}$ ke wakiltar asarar koyon da aka saba da ita kuma $\mathcal{L}_{physics}$ yana tilasta daidaiton fiziki ta hanyar ɓangarori bambanta daidaitawa waɗanda ke tafiyar da kiyaye makamashi da canja wurin zafi.

2.2 Tsarin Digital Twin

Digital Twin yana ƙirƙirar kwafin kama-da-wane na yanayin ginin zahiri, ana ci gaba da sabunta shi tare da bayanan lokaci-lokaci daga na'urori masu auna firikwensin IoT, mita na wayo, da na'urori masu sa ido kan muhalli. Wannan yana ba da damar:

• Kwaikwayo da hasashe na ainihi
• Gwajin yanayi da ingantawa
• Ci gaba da inganta tsari
• Gano abin da bai dace ba da bincike

2.3 Haɗin Tsaro na Blockchain

Fasahar Blockchain tana tabbatar da amintaccen sadarwa da bayyana gaskiya a cikin kayan aikin wayar lantarki na wayo ta hanyar samar da:

• Rikodin ma'amaloli marasa canzawa
• Ajiye bayanai masu rarrabawa
• Amincewar sadarwar tsakanin takwarorinsu
• Hanyoyin bincike masu bayyana gaskiya

3. Aiwatar da Fasaha

3.1 Tsarin Lissafi

An tsara matsalar ingantaccen amfani da makamashi a matsayin matsalar ragewa mai ƙuntatawa:

$\min_{u(t)} \int_{0}^{T} [C(t) \cdot P(t) + \alpha \cdot (T_{comfort} - T_{actual})^2] dt$

Bisa ga ƙuntatawa na zahiri ciki har da kiyaye makamashi:

$\frac{dE}{dt} = P_{generation} - P_{consumption} - P_{loss}$

Kuma yanayin zafi wanda ke ƙarƙashin:

$\rho C_p \frac{\partial T}{\partial t} = \nabla \cdot (k \nabla T) + Q_{internal} + Q_{solar} - Q_{loss}$

3.2 Ginin Tsarin Ƙirar

Ginin cibiyar jijiyoyin lantarki ya ƙunshi:

• Layin shigarwa: Neurons 128 suna sarrafa bayanan firikwensin
• Layukan ɓoye: Layer 5 tare da neurons 256-512 kowanne
• Layukan da ke da ƙididdigar fiziki: Layer 3 suna tilasta dokokin kiyayewa
• Layin fitarwa: Mafi kyawun siginonin sarrafawa don kayan aiki

4. Sakamakon Gwaji

An tabbatar da tsarin ta amfani da cikakkun bayanai da suka haɗa da bayanan amfani da makamashi na mita na wayo, fitar da makamashin sabuntawa, farashin canzawa, da abubuwan da masu amfani suka fi so. Ma'auni masu mahimmanci na aiki:

Binciken kwatancen da tsarin al'ada (Layin Regression, Daji bazuwar, SVM, LSTM, XGBoost) ya nuna mafi girman aiki a duk ma'auni, musamman a cikin daidaitawar ainihi da sarrafa yanayin canzawa.

5. Aiwatar da Lambar

Ainihin aiwatar da PINN don ingantaccen amfani da makamashi:

import tensorflow as tf
import numpy as np

class PINNEnergyOptimizer:
    def __init__(self, layers):
        self.model = self.build_model(layers)
        self.optimizer = tf.optimizers.Adam(learning_rate=0.001)
    
    def physics_loss(self, t, T, P):
        with tf.GradientTape() as tape:
            tape.watch(t)
            T_pred = self.model(t)
        
        dT_dt = tape.gradient(T_pred, t)
        
        # Ƙuntatawa daidaitawar zafi
        physics_residual = dT_dt - (P - self.alpha * (T_pred - T_env))
        
        return tf.reduce_mean(tf.square(physics_residual))
    
    def train_step(self, t_data, T_data, P_data, t_physics):
        with tf.GradientTape() as tape:
            # Asarar bayanai
            T_pred = self.model(t_data)
            data_loss = tf.reduce_mean(tf.square(T_pred - T_data))
            
            # Asarar fiziki
            physics_loss = self.physics_loss(t_physics, T_pred, P_data)
            
            # Jimillar asara
            total_loss = data_loss + self.lambda_phy * physics_loss
        
        gradients = tape.gradient(total_loss, self.model.trainable_variables)
        self.optimizer.apply_gradients(zip(gradients, self.model.trainable_variables))
        
        return total_loss, data_loss, physics_loss

6. Ayyuka na Gaba

Tsarin PINN-DT yana da babbar dama don faɗaɗawa:

• Aiwatarwa a Sikelin Birane: Ƙaruwa zuwa tsarin sarrafa makamashi na matakin birni
• Haɗin Makamashin Sabuntawa: Ingantaccen hasashe da sarrafa albarkatun hasken rana da iska
• Haɗin Motocin Lantarki: Haɗin caji na wayo tare da buƙatun makamashi na gini
• Ingantaccen Amfani da Gine-gine Daban-daban: Raba makamashi da ingantawa tsakanin gine-gine da yawa
• Jurewa Yanayi: Daidaitawa ga abubuwan da suka faru na yanayi mai tsanani da tasirin canjin yanayi

7. Bayanan

1. Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2019). Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics, 378, 686-707.
2. Glaessgen, E., & Stargel, D. (2012). The digital twin paradigm for future NASA and US Air Force vehicles. In 53rd AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics and materials conference (p. 1818).
3. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.
4. Wang, H., Lei, Z., Zhang, X., Zhou, B., & Peng, J. (2019). A review of deep learning for renewable energy forecasting. Energy Conversion and Management, 198, 111799.
5. Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system. Decentralized Business Review, 21260.