Blockchain da AI Fasahohi don Masu Amfani a cikin Wayoyi Masu Hikima: Cikakken Bita

2.1 Carbon Pricing Mechanisms

Farashin carbon ta hanyar tsarin cinikin hayaki ko harajin carbon yana ƙarfafa canji daga samarwar mai da man fetur zuwa hanyoyin sabuntawa. Waɗannan hanyoyin suna shigar da farashin muhalli na hayakin carbon, wanda ke sa makamashin sabuntawa ya fi gasa ta tattalin arziki.

2.2 Regulatory Support Structures

Dole ne tsarin ka'idoji su inganta don ɗaukar kwararar makamashi mai zuwa biyu kuma su gane masu amfani a matsayin mahalarta masu aiki a cikin kasuwannin makamashi. Wannan ya haɗa da manufofin awo na gida, tarif ɗin ciyarwa, da ka'idodin shiga kasuwa.

3.1 Cinikin Makamashi Tsakanin Mutum da Mutum

Blockchain yana baiwa masu amfani da makamashi kai tsaye ciniki tsakanin juna ba tare da matsakaici ba, yana rage farashin ciniki da kuma haɓaka ingantaccen kasuwa. Smart contracts suna sarrafa hanyoyin sasantawa ta atomatik kuma suna tabbatar da amincin ciniki.

3.2 Decentralized Market Structures

Kasuwancin makamashi na Decentralized da aka gina akan fasahar blockchain suna ba da ƙarfin juriya, gaskiya, da samun dama idan aka kwatanta da tsarin kasuwa na gargajiya na tsakiya.

4.1 State Monitoring and Prediction

Algorithms na koyon inji, musamman samfuran zurfin koyo, suna ba da damar yin hasashen daidaitaccen samar da makamashi mai sabuntawa, tsarin lodi, da farashin kasuwa. Waɗannan hasashe suna da mahimmanci don mafi kyawun aikin tsarin da yanke shawarar cinikin makamashi.

4.2 Optimal Decision Making

Reinforcement learning and optimization algorithms support real-time decision making for energy scheduling, storage management, and market participation strategies.

5.1 Mathematical Formulations

Matsalar da shirin makamashi mafi kyau ga masu amfani da kuma samar da shi za a iya tsara shi kamar haka:

Inda C_{grid} shine farashin wutar lantarki na grid, C_{gen} shine farashin samarwa na gida, R_{sell} shine kudaden shiga daga sayar da makamashi, kuma SOC yana wakiltar yanayin caji na ajiyar makamashi.

5.2 Aiwartar Algorithm

Samfurin pseudocode don sarrafa makamashi mai amfani ta amfani da koyo mai ƙarfi:

class ProsumerAgent:
    def __init__(self, state_size, action_size):
        self.state_size = state_size
        self.action_size = action_size
        self.memory = deque(maxlen=2000)
        self.gamma = 0.95
        self.epsilon = 1.0
        self.epsilon_min = 0.01
        self.epsilon_decay = 0.995
        self.learning_rate = 0.001
        self.model = self._build_model()
    
    def _build_model(self):
        model = Sequential()
        model.add(Dense(24, input_dim=self.state_size, activation='relu'))
        model.add(Dense(24, activation='relu'))
        model.add(Dense(self.action_size, activation='linear'))
        model.compile(loss='mse', optimizer=Adam(lr=self.learning_rate))
        return model
    
    def act(self, state):
        if np.random.rand() <= self.epsilon:
            return random.randrange(self.action_size)
        act_values = self.model.predict(state)
        return np.argmax(act_values[0])
    
    def replay(self, batch_size):
        minibatch = random.sample(self.memory, batch_size)
        for state, action, reward, next_state, done in minibatch:
            target = reward
            if not done:
                target = reward + self.gamma * np.amax(self.model.predict(next_state)[0])
            target_f = self.model.predict(state)
            target_f[0][action] = target
            self.model.fit(state, target_f, epochs=1, verbose=0)
        if self.epsilon > self.epsilon_min:
            self.epsilon *= self.epsilon_decay