Abstract
The Japan Electric Power Exchange (JEPX) has introduced a feed-in premium to promote the trading of renewable energy electricity in the market; thus, the exchange has become increasingly important for RE companies to maintain profitability in market trading. However, electricity prices are not only affected by directly measurable factors such as electricity demand, fuel prices, and weather but also by corporate bidding strategies, social conditions, and other human behaviors, making it difficult to predict electricity prices. Given that electricity demand is related to human behavior, this study focuses on web search behavior and clarifies the relationship between keyword search volumes and electricity market prices in Japan. Correlation and vector autoregression analyses results show a moderately strong positive correlation between the logarithmic difference of the keyword search volume and that of the electricity price. In addition, we find that the logarithmic difference of the electricity price tends to increase when that of the keyword search volume on the previous day increases. These results suggest that search volumes of specific keywords can be effective explanatory variables for area price prediction models and can help identify signs of price spikes.
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